Slot Machine Odds - Chances & Odds of Winning a Jackpot

what are the odds of winning on a penny slot machine

what are the odds of winning on a penny slot machine - win

Ended my gambling career (for now) on a high note - jackpot handpay to end 2020. My thoughts and ramblings as a now-retired gambler.

Warning: long rambling stories ahead. I am bored and waiting to get through my first day back at work since before Christmas. You've been warned!
I've been going to the casino pretty regularly for the past few years. Before that, I played occasionally. I exclusively play slots. I view it as a night out - first with friends back when I brought $50 and played penny denom minimum bet spins and prayed to win $20, and then eventually shifting my mindset to playing higher bets and denominations. I hit my first jackpot handpay a couple of years ago. I hit $3700 on a $27 bet on a Geisha machine. I've hit a few other jackpots here and there, culminating with my biggest jackpot ever this past summer. I hit $12K on a $50 bet on a Pompeii slot machine.
Well, the long story short is that I have fallen out of love with gambling. I have somehow managed to have a positive ROI on gambling. I track my withdrawls and win on a spreadsheet. To put it bluntly: I have been extremely lucky over the past few years. I know that slots are not a viable way to win money in the long run, so I made a decision a few months ago to "retire" from gambling at the end of 2020.
I went to my local casino last Wednesday. It just so happens when I hit a jackpot that I usually do it within the first half hour or so I'm at the casino. Well, it happened again. After going up $600 or so on another slot machine (I don't remember the name), I went to one of my most hated/favorite old school slots - Zeus dollar denomination. One of my worst moments in all of gambling was a few years ago. I got a bonus round on the Zeus dollar denomination on max bet of $45 a spin. I was BEYOND excited. I've seen Youtube videos where people have won tens of thousands of dollars in that exact scenario. Much to my shock, I won nothing. In that game, you don't win anything for triggering the bonus. So I actually *lost* $45 on getting the bonus. I cashed out and left immediately.
Anyway, last week I hit a modest $4500. It was exciting...but not as exciting as I thought it should be. I was cool, calm, and...detached. The wins didn't mean much to me, and the losses mean absolutely nothing. My wife and I are in the EXTREMELY fortunate position that losing $500 or so every week or two at the casino is affordable. I'm not ignorant to how lucky we are to be in this position.
After getting paid out, I played a bit longer. But that hand pay drove home the realization that I had a few months ago: it was time to stop gambling. If I can't get pumped about a big win like that, and if I'm not even phased a little bit by losing, it's just not worth gambling any more. I used to go for entertainment, but even now gambling doesn't provide that much.
As I sit now, I am up roughly $18K over three years of slots. Not bad, but not life changing. Enough that I bought my wife a Burberry and Louis Vuitton handbag on separate occasions. The rest if stashed in savings or in an investment account somewhere. But I am 100% committed to being done. At least for 2021, and probably longer.
If anyone is interested in hearing my thoughts on how to win...I don't have any insight to share. It's luck. I got lucky. I know I got lucky. The usual tropes about setting win and loss thresholds is good advice. Sometimes I chased payouts and hit them. Sometimes I chased and lost. But I managed to hit more than miss, and for that I'm lucky. And thankful.
Anyway. I don't have a major takeaway or anything. I don't have many people I can talk about this with in my personal life, so I figured I'd share a bit of my story here.
If you do gamble, please do so responsibly. Good luck, and try to have fun. If you're not having fun, it's probably not the right way to spend your time or money.
EDIT:
I just wanted to say to anyone who reads this in the future that I appreciate the nice responses and PMs from people. It's nice to share a positive experience with others! I sincerely hope that if any of you choose to play in the future, you choose to do so responsibly. Gambling can be a hugely problematic lifestyle for some people. Stay safe. (end of preaching here).
I also want to take a second to address some comments from some people about slots being skill based. This is 100% false. The concept of slots being skill based in any way is demonstrably untrue with three seconds of reasonable thinking. If we accept that there is a hypothetical slot game which is based on skill and not pure luck, what are the consequences of that? First of all, this information would leak out. There would be no way to contain it. If one person can solve the system, another an as well. Subsequently, someone would write a book on the subject. Think about all the poker and blackjack strategy books out there. These are games where skillful play can increase your odds of winning. Last I checked, there aren't any books or Supersytem-level analyses from prominent individuals willing to stake their names and reputations on publishing a "slot technique" book. There's a reason for this. And also - think about this: casinos still carry blackjack tables for a reason: they still have an edge to win. If there is a surefire way for individuals to win when playing slots, casinos would 100% for sure take these games out of circulation. Casinos are not in the business of giving away money. Any claim there is a foolproof way to win money playing slots does not make sense when critical thinking is applied to the circumstances.
Slots are not like card games. Finding and playing only games where there is a "must win by" progressive is not the same thing as skillful play. That's more akin to something like card counting in blackjack. Many people who design slot machines and engineer the software behind the scenes have posted on Reddit and elsewhere that wins are based on random number generators running behind every spin. There is literally no skill involved - you win or lose each spin based on pure random luck.
I am saying this because there are a number of people who come to this subreddit to look for ways to cheat the system and get easy money. I see posts like this fairly often, and I'm only browsing this subreddit occasionally. Gambling is not, and should not, be a way for anyone looking to make a quick buck. If you're looking to get an edge playing slots because you need to pay bills or make a quick buck, you are already in serious trouble. Do not buy into the delusion that you can get an edge or guarantee a win. People saying this are snake oil salesmen who do not care for you or your well-being.
Anyway. I'm going to stop monitoring this post. I'm still open to receiving PMs or messages, but I've had my fun with this so far. I could do with fewer trolls, but this is the internet. I knew what to expect. Bon chance, everyone!
submitted by Creepy_Zucchini6387 to gambling [link] [comments]

20 Overlooked Single Player Indie Games

Introduction
We're all familiar with the Hotline Miami's, Hollow Knight's, and Celeste's of the world. These are some of the indie games that hit the big time. Of course, for every one of these games, there's 100 other indie games that have been glossed over, relegated to a spot in a digital store few people will ever find themselves in. I wanted to bring attention to some of these lesser known indie games.
I'm going to order them according to Metacritic Critic Ratings. Some of the games towards the bottom have a pretty low rating that I personally disagree with, but it's only fair that you hear from more than just me. While the reviews are low for some games, this is partly due to how few reviews there are for some games. #19 on the list has a 49% for the Xbox One version of the game due to it only having two reviews, while the PlayStation 4 version has a 90% rating due to it only having one review, despite both versions being functionally the same. This high level of variance usually occurs when a game only has a few reviews.
Price will include a link to the U.S. store page of the game. Price is in U.S. dollars.
1. Inertial Drift
2. Cursed Castilla (Maldita Castilla EX)
3. Valfaris
4. Pumpkin Jack
5. Pato Box
6. Ultra Hat Dimension
7. Momodora: Reverie Under the Moonlight
8. The Count Lucanor
9. Late Shift
10. Unbox: Newbie’s Adventure
11. Spark the Electric Jester 2
12. Remothered: Tormented Fathers
13. Four Sided Fantasy
14. SINNER: Sacrifice for Redemption
15. Tamashii
16. Verlet Swing
17. Warlock’s Tower
18. The Bunker
19. Hayfever
20. Cybarian: The Time-Traveling Warrior
Conclusion
My top 5 on the list in order would be the following: (1.) Hayfever, (2.) Valfaris, (3.) Cursed Castilla: (Maldita Castilla EX), (4.) Momodora: Reverie Under the Moonlight, and (5.) Pumpkin Jack.
Have you played any of these games? What are some other overlooked single player indie games?
See my post below for some upcoming indie games to look out for.
submitted by Underwhere_Overthere to XboxSeriesX [link] [comments]

20 Overlooked Single Player Indie Games

We're all familiar with the Hotline Miami's, Hollow Knight's, and Celeste's of the world. These are some of the indie games that hit the big time. Of course, for every one of these games, there's 100 other indie games that have been glossed over, relegated to a spot in a digital store few people will ever find themselves in. I wanted to bring attention to some of these lesser known indie games.
I'm going to order them according to Metacritic Critic Ratings. Some of the games at the bottom have pretty low critic ratings. I personally disagree with the low scores of these games, but it's only fair that you hear from more than just me. Keep in mind that games with only one or two User Ratings on Metacritic will not show the score. A game needs at least three User Ratings on Metacritic before the score will be shown. This is not the case for Critic Reviews.
Price will contain the U.S. PlayStation Store link to the game.
1. Hayfever
2. Valfaris
3. Four Sided Fantasy
4. Bleep Bloop
5. Horizon Shift ‘81
6. Daggerhood
7. Momodora: Reverie Under the Moonlight
8. Ultra Hat Dimension
9. Remothered: Tormented Fathers
10. Reverie
11. Inertial Drift
12. Cursed Castilla (Maldita Castilla EX)
13. Pato Box
14. The Count Lucanor
15. The Bunker
16. A Tale of Paper
17. Late Shift
18. SINNER: Sacrifice for Redemption
19. Verlet Swing
20. Neon Drive
Conclusion
My top 5 on the list in order would be the following: (1.) Hayfever, (2.) Valfaris, (3.) Cursed Castilla: (Maldita Castilla EX), (4.) Momodora: Reverie Under the Moonlight, and (5.) Bleep Bloop.
Have you played any of these games? What are some other overlooked single player indie games?
If you’re looking for more indie games to play, see my post here:
submitted by Underwhere_Overthere to PS5 [link] [comments]

Giants final report card: Grading every player from Logan Ryan to Golden Tate - The Athletic

The Giants coaching staff spent last week doing their final evaluations on the 2020 season. I figured I’d do the same, so here are my grades for every player who played at least 10 percent of the snaps on offense or defense this season.
An important note: Expectations are factored into my grading scale. Daniel Jones is obviously a better player than C.J. Board, but they got the same grade based on how their performances measured up to preseason expectations.
QUARTERBACKS
• Daniel Jones: C. Jones didn’t make “the leap” often experienced by second-year quarterbacks. Most of Jones’ numbers were down despite making 14 starts this season compared to 12 as a rookie. Jones’ drop in touchdown passes (24 to 11) was jarring, although he also cut back on turnovers (12 interceptions and 18 fumbles in 2019, 10 interceptions and 11 fumbles in 2020). This coaching staff leaned more heavily on Jones as a runner and he proved to be a legitimate threat on the ground.
There are endless reasons/excuses for the struggles of the offense, and by extension, Jones this season. How much the scheme, protection, skill players or reduced offseason affected Jones is impossible to quantify. But in a production business, the Giants offense was a failure. The quarterback has to bear some responsibility for that.
The coaching staff is fully committed to Jones as the quarterback of the future. It’s clear that coaches believe Jones has the intangibles to lead the team and they observed subtle improvements in the second half of the season. But he needs more production in his third season to validate that faith.
• Incomplete: Colt McCoy, Joe Webb. McCoy was exactly as expected: A solid veteran behind the scenes who could fill in competently in a spot start (he was the quarterback for the biggest win of the season over Seattle) but with physical limitations that make him best served as a backup.
RUNNING BACKS
• Devonta Freeman: C-. Freeman clearly wasn’t the player he was earlier in his career in Atlanta. He only averaged 3.2 yards per carry, but he had the misfortune of being the lead back before the offensive line hit its stride. Freeman never returned after suffering an ankle injury in Week 7.
• Wayne Gallman: B+. Gallman was buried by Pat Shurmur’s staff and then overlooked by Joe Judge until Freeman’s injury. Gallman took over as the lead back in Week 8 and thrived. His north-south style meshed with the offensive line and he always finished runs by falling forward. Gallman doesn’t bring much to the passing game, but he proved he’s a legitimate NFL back.
• Dion Lewis: D. Lewis clearly wasn’t capable of replacing Saquon Barkley, but the veteran underwhelmed in his primary role as a third-down back. Lewis had three fumbles, including two on kickoffs. He’s a good locker room presence who had familiarity with Judge, but the Giants need an upgrade next season.
• Alfred Morris: B. There were no expectations when the Giants signed the 32-year-old Morris to the practice squad in Week 4, but injuries eventually opened the door for a role. Morris has clearly lost a step from his Pro Bowl days, but the savvy veteran was surprisingly effective, averaging 4.3 yards per carry.
• Incomplete: Saquon Barkley, Eli Penny. Barkley’s torn ACL early in Week 2 was a crushing blow to an offense built around the dynamic back. Barkley has a lengthy rehab ahead, but expect him to come back loaded with motivation. The run game improved as Penny’s playing time increased in the middle of the season, but the fullback’s role was limited in this offense.
WIDE RECEIVERS
• C.J. Board: C. Board met low expectations. He was an afterthought as a receiver and didn’t make an impact on special teams.
• Austin Mack: B-. It’s an accomplishment for an undrafted rookie just to get playing time. Mack made an immediate impression with four catches for 72 yards when starting in Golden Tate’s place in Week 9, but otherwise was a non-factor as a receiver aside from a crucial third-down drop in Week 16. Mack’s aggressive blocking stood out. He likely needs to become a special teams contributor to carve out a role.
• Damion Ratley: D. The Giants chose to sign Ratley rather than keeping one of the receivers who impressed in camp. Ratley’s most notable contribution in five games was an offensive pass interference penalty that negated a touchdown in a loss to the Cowboys and led to his release.
• Sterling Shepard: B. I wrote before the season to “expect around 70 catches and 800 yards if Shepard stays healthy.” Shepard finished with 66 catches for 656 yards despite missing four games with turf toe. Shepard has proven to be a quality No. 2/3 receiver during his five seasons. The problem is he’s been thrust into a No. 1 role the past two seasons. Ideally, Shepard will slide back into the slot and complement big-play threats outside next season. He’s a useful player to have in that role.
• Darius Slayton: B. Slayton’s numbers in his second season were nearly identical to his rookie year (50 catches for 751 yards in 2020, 48 catches for 740 yards in 2019). His touchdown rate predictably regressed; he had three this season after having eight as a rookie. This season felt like a slight disappointment since expectations were high after his impressive rookie season, but Slayton has still been a steal as a fifth-round pick. It’s impossible to know how much Slayton was limited by injuries, but it seems clear that he’s not a No. 1 receiver. He could be a solid deep threat to complement a true No. 1.
• Golden Tate: D. It was evident in training camp that Tate’s role was going to be reduced. It was also predictable that the accomplished veteran wouldn’t handle that well. That friction came to a head when Tate was suspended for a game in the middle of the season after complaining about his role. His production plummeted this season, although he was oddly effective at making contested catches. Tate’s season came to a premature end when he suffered a calf injury in practice in Week 16. It’s a safe bet that the 32-year-old has played his final game in a Giants uniform since the team can create $6 million in cap savings if they cut him.
• Incomplete: Dante Pettis. Pettis had to wait until Week 16 for an opportunity after getting claimed in Week 10. The 2018 second-round pick made an impact in two games, which is encouraging heading into next season.
TIGHT ENDS
• Evan Engram: C-. It’s difficult to grade Engram objectively because his lows were so low, but he still was a productive player. His Pro Bowl selection was a farce, but his 63 catches and 654 yards ranked among the NFC leaders at tight end. But those numbers don’t tell the full story, as Engram was directly involved in a disproportionate number of turnovers and he was inefficient considering he was the fourth-most targeted tight end in the league. Engram works hard but it should be evident by now that he’ll never be a competent blocker. The Giants can bring Engram back for 2021 on his fifth-year option, but a change of scenery may be best for all involved.
• Kaden Smith: B. Smith didn’t take the next step after showing promise in place of Engram late in the 2019 season. Smith wasn’t a big part of the passing game but he remains a solid blocker, especially on the counter runs that became a staple of the offense. His ceiling is being a No. 2 tight end and he fills that role capably.
• Levine Toilolo: C-. It was always strange that the Giants gave Toilolo a two-year, $6.2 million contract with $3.2 million guaranteed. It didn’t make any more sense after Toilolo played just 27 percent of the offensive snaps. Toilolo was a decent blocker, while getting just six targets. He’ll likely be cut for $3 million in cap savings this offseason.
• Incomplete: Eric Tomlinson. Tomlinson was mostly a healthy scratch before getting cut in Week 10 and landing in Baltimore.
OFFENSIVE LINE
• Cameron Fleming: C-. Fleming was signed to be a swing tackle but was pressed into a starting job by Nate Solder’s opt out. Fleming wasn’t good, but his performance was in line with his established level of play. If nothing else, Fleming provided some reliability as a veteran.
• Nick Gates: B. Gates made tremendous progress after being thrust into the starting center job with no game experience (or even a full offseason at the position). It’s hard to evaluate the mental aspect of the position and that’s surely an area that will grow with experience. But Gates looked comfortable in the middle of the line and embraced an enforcer’s role. He showed enough to provide optimism that he can develop into a quality center.
• Will Hernandez: C-. Hernandez’s play was similar to last season, which was a disappointment since he hasn’t built on a promising rookie season in 2018. He lost the starting left guard job after missing two games with COVID-19 in the middle of the season. It doesn’t seem like Hernandez is a big piece of the new coaching staff’s plans.
• Shane Lemieux: C. Lemieux was a fifth-round pick, so expectations were low. He took over as at left guard after Hernandez missed time with COVID-19, starting the final nine games. Lemieux plays with a nasty streak that is evident in his run blocking, but he has a long way to go in pass protection. The Giants need to decide if they believe Lemieux is ready to be a full-time starter next season.
• Matt Peart: B. Peart was viewed as a developmental project, so it was encouraging that the third-round pick showed enough to merit snaps in a rotation with Fleming at right tackle. Peart looked promising early but his play fell off in the second half of the season after he missed one game with COVID-19 and dealt with an ankle injury. Peart didn’t play a single snap in the season finale, leaving questions about how he’s viewed by the coaching staff.
• Andrew Thomas: C. Thomas predictably experienced growing pains in his first season as a starting left tackle. The No. 4 pick had a particularly rough transition to the NFL in the first half of the season, but he made strides in the second half. The view of Thomas was hurt by comparisons to the other rookie tackles picked after him, but he showed enough signs to indicate he can be the long-term answer at left tackle.
• Kevin Zeitler: B. Zeitler’s play may have slipped a bit, but the 30-year-old remained a reliable, durable, low-maintenance player. The Giants have a big decision looming on Zeitler and his $14.5 million cap hit for 2021.
• Incomplete: Jackson Barton, Kyle Murphy, Spencer Pulley, Chad Slade. The line stayed remarkably healthy aside from a few COVID-19 related absences, so these backups never got an opportunity to play.
DEFENSIVE LINE
• B.J. Hill: B. Hill was effective in a limited role after being relegated to backup duty by the additions of Dexter Lawrence and Leonard Williams last year. Hill has another year on his rookie deal so he provides an insurance policy if Williams or Dalvin Tomlinson leave in free agency.
• Austin Johnson: B+. Johnson was a perfect fit in the role he was signed to fill. He was solid against the run and made a few big plays early in the season. He’s worth bringing back on a similar low-cost deal.
• Dexter Lawrence: B+. I predicted Lawrence would have four sacks after recording 2.5 as a rookie. He hit that total, as he made strides as a pass rusher. Lawrence’s strength remains run defense. The 23-year-old is a solid piece in the middle of the defense to build around.
• Dalvin Tomlinson: A-. The Giants know exactly what they’re getting from Tomlinson: A strong run defender who will be in the lineup every week and be a positive influence in the locker room. Tomlinson continues to show more as a pass rusher, recording 3.5 sacks for the second consecutive season and setting a career-high with 28 pressures. Tomlinson has earned a big free-agent contract; the question is if he’ll get it from the Giants.
• Leonard Williams: A+. This was the player the Jets thought they were getting with the sixth pick in the 2015 draft. Williams broke through with a career-high 11.5 sacks in his sixth season. Williams has always pressured quarterbacks, but sacks get players paid and he’s now in line for a deal worth $20 million per year. He’s young and durable, which will help the Giants feel comfortable making that type of commitment, although there should be an understanding that his sack production is likely unsustainable.
• Incomplete: R.J. McIntosh. The defensive line didn’t suffer any injuries so McIntosh was a healthy scratch all season.
OUTSIDE LINEBACKERS
• Lorenzo Carter: B. Carter looked like a good fit in Patrick Graham’s defense before tearing his Achilles in Week 5. But there still haven’t been any signs that he’ll ever be a top edge rusher. Carter could be a solid complement to a No. 1 edge rusher, which is a reasonable return for a third-round pick.
• Carter Coughlin: B. Injuries at edge rusher forced Coughlin into a bigger role than expected as a rookie. He flashed potential, although the seventh-round pick’s limitations were evident in an expanded role. Coughlin could grow into a Kyler Fackrell type of player.
• Kyler Fackrell: B-. Fackrell made a few splash plays early, but he was quiet in the second half of the season before missing four games with a calf injury. The Giants got a decent return on the prove-it deal they gave Fackrell, but he’s not a difference maker.
• Markus Golden: B. The coaching staff showed no interest in Golden, dating back to when he sat in free agency until the team applied the seldom used unrestricted free agent tender. Golden was a productive pass rusher in limited opportunities despite being buried behind less accomplished players before a midseason trade to Arizona.
• Jabaal Sheard: C+. The Giants needed a legitimate NFL edge defender after injuries and the Golden trade decimated the position. Sheard made a few plays, most notably a late strip sack in a win over the Bengals, but otherwise made minimal impact.
• Incomplete: Oshane Ximines, Cam Brown, Niko Lalos, Trent Harris. Ximines had a quiet start before suffering a season-ending shoulder injury in Week 4. Brown made a solid impression on special teams and flashed pass rush potential in limited opportunities, but he needs to get bigger to be an impactful player on defense. Lalos turned heads with a pair of takeaways in his first two games, but he didn’t get enough playing time to make a bigger impact.
INSIDE LINEBACKERS
• Tae Crowder: B. Considering he was Mr. Irrelevant, it was impressive that Crowder started six games next to Blake Martinez at inside linebacker. Crowder showed promise, particularly with his speed. But there’s plenty of room for improvement when it comes to taking on blocks and filling gaps consistently. Giants fans should be careful not to go overboard with expectations for Crowder as happened with Ryan Connelly a year ago.
• Devante Downs: C-. It’s hard to understand what the staff saw to make Downs a starter out of training camp. Downs’ performance was what was expected from a player who didn’t play a single defensive snap in first two NFL seasons. Downs needs to be a backup in the future.
• Blake Martinez: A. I predicted 140 tackles for Martinez; he was even more productive, finishing third in the league with 151 tackles. There was some skepticism about the three-year, $30.75 million contract the Giants gave Martinez in the offseason, but he was better than expected. In addition to being a tackling machine, Martinez was effective as a blitzer and wasn’t a liability in coverage. Martinez gives the Giants an ultra-reliable leader in the middle of their defense.
• David Mayo: C. Mayo shifted back to the reserve role that he’s suited for. The coaching staff oddly tried to use Mayo on the edge at times but he didn’t have the ability to fill that role.
• Incomplete: TJ Brunson. Brunson played a handful of special teams snaps in a few games, but mostly was a healthy scratch.
CORNERBACKS
• James Bradberry: A+. Imagine the Giants defense without Bradberry this season. If the nausea from that thought has passed, you can appreciate the impact Bradberry made in his first season in New York. Bradberry proved capable of eliminating opposing No. 1 receivers, although he wasn’t always asked to do so as the defense grew more zone heavy. Bradberry had three interceptions and 18 passes defensed, which ranked second in the league. Bradberry established himself as a premier cornerback after signing a three-year, $43.5 million contract with the Giants.
• Darnay Holmes: B. Like most rookie corners, Holmes struggled early in the season. He started to find his groove before a knee injury cost him three games late in the season. Holmes was flagged too frequently, and his penalties always seemed to come in costly moments. Holmes’ role could be reduced by the Giants’ commitment at safety.
• Ryan Lewis: C+. Lewis was signed because he’s a favorite of Patrick Graham. Lewis added some stability to the No. 2 corner spot, but he got beat deep late in losses to Dallas and Philadelphia. Lewis suffered a hamstring injury in Week 8 and missed the rest of the season. He’s best suited as a backup.
• Isaac Yiadom: B-. The Giants’ desperation at cornerback prompted a trade of a seventh-round pick to Denver for Yiadom late in camp. Yiadom struggled early in the season before providing some solid play after getting a second chance midway through the season. Yiadom was benched for the season finale after his play regressed. Like Lewis, Yiadom should be viewed as a backup.
• Incomplete: Madre Harper, Brandon Williams, Jarren Williams, Corey Ballentine. The Giants’ plan to start Ballentine at No. 2 corner was doomed from the outset and he was benched after two games and then cut in Week 10.
SAFETIES
• Julian Love: B. Love is the defensive version of Gallman. The coaching staffs in each of the past two seasons have been hesitant to play Love, but he’s been solid when given opportunities. Love filled in admirably twice at cornerback late in the season. He lacks the speed to be a full-time answer at corner, but he should be the ideal backup to Logan Ryan as a defensive back who can fill multiple roles.
• Jabrill Peppers: B+. Peppers had the best season of his career, showing flashes reminiscent of Landon Collins when the former Giant was on top of his game. Peppers was a physical presence around the line of scrimmage and a punishing tackler when he lined up ball carriers. Peppers is never going to be great in coverage, but this staff minimized his one-on-one matchups and he seemed more comfortable in the zone-heavy scheme. Peppers was productive as a punt returner and he brings an energy that the defense needs.
• Logan Ryan: B+. Ryan earned an A+ in intangibles, serving as a great model for younger players and providing vocal leadership buttressed by a pair of Super Bowl rings. Ryan was extension of coordinator Patrick Graham on the field, while his versatility allowed for varied looks defensively week-to-week. Ryan’s actual play was a bit uneven at times. He displayed a knack for big plays, particularly with forcing turnovers, but his tackling was spotty.
• Incomplete: Xavier McKinney, Nate Ebner, Adrian Colbert, Montre Hartage, Sean Chandler. McKinney provided a glimpse of his potential in the final six games after missing the first 10 games of his rookie season with a broken foot. Ebner’s limited defensive playing time reinforced that he should only be a special teamer.
SPECIALISTS
• Riley Dixon: C. It was a disappointing season for Dixon, who had been consistent in his first two seasons with the Giants. Dixon didn’t have the same knack for pinning opponents deep (9.2 percent of punts for touchbacks this season compared to 2.9 percent in 2019). That decline can partly be attributed to subpar gunners in coverage, but the Giants need Dixon to get back on track.
• Graham Gano: A+. Gano was lights out. His lone missed field goal was a 57-yarder in Week 2. He then made a franchise record 30 straight field goals. Gano made 5 of 6 field goals from 50-plus yards making him a weapon for a low-scoring team.
• Casey Kreiter: A. Kreiter’s name wasn’t mentioned once all season. That’s evidence of a job well done for a long snapper.
COACH/GM
• Joe Judge: A-. Since I’m weighing preseason expectations, Judge grades out well. I predicted the Giants would win five games so 6-10 can’t be viewed as a disappointment for Judge. Think about the obstacles he was facing as a first-time head coach in an unprecedented season with a flawed roster. Judge took charge from Day 1 and impressively got players to buy in, even when the team started 1-7.
Judge dealt with brushfires — suspending Tate, firing offensive line coach Marc Colombo — but always seemed in control. His game management was sound, although he skewed conservative on fourth downs. The defensive coaching was stellar and it’s clear that Judge and Graham have an excellent working relationship. On the other side, Judge assumes some responsibility for the No. 31 scoring offense and he needs to get that fixed for next season.
Judge succeeded in laying a foundation in his first season. The grading curve will get steeper next season when there will be higher expectations for tangible results.
• Dave Gettleman: B. Gettleman gets an A+ for free agency, as Bradberry, Martinez, Ryan and Gano were game-changing additions. The draft is harder to assess. Many of the rookies played this season, but none were clear stars. Time will tell on this draft class.
Gettleman’s grade is lower than Judge’s since this wasn’t his first season. It’s impossible to separate previous years when assessing a general manager. And the many holes on the roster that Judge had to compensate for is a reflection of the job Gettleman has done in his three years as GM.
Co-owner John Mara is encouraged by the working relationship of Gettleman and Judge. Their first year together was promising, but Gettleman needs another strong offseason to overcome the errors of his past.
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submitted by puwanon to u/puwanon [link] [comments]

NVidia – Know What You Own

How many people really understand what they’re buying, especially when it comes to highly specialized hardware companies? Most NVidia investors seem to be relying on a vague idea of how the company should thrive “in the future”, as their GPUs are ostensibly used for Artificial Intelligence, Cloud, holograms, etc. Having been shocked by how this company is represented in the media, I decided to lay out how this business works, doing my part to fight for reality. With what’s been going on in markets, I don’t like my chances but here goes:
Let’s start with…
How does NVDA make money?
NVDA is in the business of semiconductor design. As a simplified image in your head, you can imagine this as designing very detailed and elaborate posters. Their engineers create circuit patterns for printing onto semiconductor wafers. NVDA then pays a semiconductor foundry (the printer – generally TSMC) to create chips with those patterns on them.
Simply put, NVDA’s profits represent the difference between the price at which they can sell those chips, less the cost of printing, and less the cost of paying their engineers to design them.
Notably, after the foundry prints the chips, NVDA also has to pay (I say pay, but really it is more like “sell at a discount to”) their “add-in board” (AIB) partners to stick the chips onto printed circuit boards (what you might imagine as green things with a bunch of capacitors on them). That leads to the final form in which buyers experience the GPU.
What is a GPU?
NVDA designs chips called GPUs (Graphical Processing Units). Initially, GPUs were used for the rapid processing and creation of images, but their use cases have expanded over time. You may be familiar with the CPU (Central Processing Unit). CPUs sit at the core of a computer system, doing most of the calculation, taking orders from the operating system (e.g. Windows, Linux), etc. AMD and Intel make CPUs. GPUs assist the CPU with certain tasks. You can think of the CPU as having a few giant very powerful engines. The GPU has a lot of small much less powerful engines. Sometimes you have to do a lot of really simple tasks that don’t require powerful engines to complete. Here, the act of engaging the powerful engines is a waste of time, as you end up spending most of your time revving them up and revving them down. In that scenario, it helps the CPU to hand that task over to the GPU in order to “accelerate” the completion of the task. The GPU only revs up a small engine for each task, and is able to rev up all the small engines simultaneously to knock out a large number of these simple tasks at the same time. Remember the GPU has lots of engines. The GPU also has an edge in interfacing a lot with memory but let’s not get too technical.
Who uses NVDA’s GPUs?
There are two main broad end markets for NVDA’s GPUs – Gaming and Professional. Let’s dig into each one:
The Gaming Market:
A Bit of Ancient History (Skip if impatient)
GPUs were first heavily used for gaming in arcades. They then made their way to consoles, and finally PCs. NVDA started out in the PC phase of GPU gaming usage. They weren’t the first company in the space, but they made several good moves that ultimately led to a very strong market position. Firstly, they focused on selling into OEMs – guys like the equivalent of today’s DELL/HP/Lenovo – , which allowed a small company to get access to a big market without having to create a lot of relationships. Secondly, they focused on the design aspect of the GPU, and relied on their Asian supply chain to print the chip, to package the chip and to install in on a printed circuit board – the Asian supply chain ended up being the best in semis. But the insight that really let NVDA dominate was noticing that some GPU manufacturers were focusing on keeping hardware-accelerated Transform and Lighting as a Professional GPU feature. As a start-up, with no professional GPU business to disrupt, NVidia decided their best ticket into the big leagues was blowing up the market by including this professional grade feature into their gaming product. It worked – and this was a real masterstroke – the visual and performance improvements were extraordinary. 3DFX, the initial leader in PC gaming GPUs, was vanquished, and importantly it happened when funding markets shut down with the tech bubble bursting and after 3DFX made some large ill-advised acquisitions. Consequently 3DFX, went from hero to zero, and NVDA bought them for a pittance out of bankruptcy, acquiring the best IP portfolio in the industry.
Some more Modern History
This is what NVDA’s pure gaming card revenue looks like over time – NVDA only really broke these out in 2005 (note by pure, this means ex-Tegra revenues):
📷 https://hyperinflation2020.tumblr.com/private/618394577731223552/tumblr_Ikb8g9Cu9sxh2ERno
So what is the history here? Well, back in the late 90s when GPUs were first invented, they were required to play any 3D game. As discussed in the early history above, NVDA landed a hit product to start with early and got a strong burst of growth: revenues of 160M in 1998 went to 1900M in 2002. But then NVDA ran into strong competition from ATI (later purchased and currently owned by AMD). While NVDA’s sales struggled to stay flat from 2002 to 2004, ATI’s doubled from 1Bn to 2Bn. NVDA’s next major win came in 2006, with the 8000 series. ATI was late with a competing product, and NVDA’s sales skyrocketed – as can be seen in the graph above. With ATI being acquired by AMD they were unfocused for some time, and NVDA was able to keep their lead for an extended period. Sales slowed in 2008/2009 but that was due to the GFC – people don’t buy expensive GPU hardware in recessions.
And then we got to 2010 and the tide changed. Growth in desktop PCs ended. Here is a chart from Statista:
📷https://hyperinflation2020.tumblr.com/private/618394674172919808/tumblr_OgCnNwTyqhMhAE9r9
This resulted in two negative secular trends for Nvidia. Firstly, with the decline in popularity of desktop PCs, growth in gaming GPUs faded as well (below is a chart from Jon Peddie). Note that NVDA sells discrete GPUs, aka DT (Desktop) Discrete. Integrated GPUs are mainly made by Intel (these sit on the motherboard or with the CPU).
📷 https://hyperinflation2020.tumblr.com/private/618394688079200256/tumblr_rTtKwOlHPIVUj8e7h
You can see from the chart above that discrete desktop GPU sales are fading faster than integrated GPU sales. This is the other secular trend hurting NVDA’s gaming business. Integrated GPUs are getting better and better, taking over a wider range of tasks that were previously the domain of the discrete GPU. Surprisingly, the most popular eSports game of recent times – Fortnite – only requires Intel HD 4000 graphics – an Integrated GPU from 2012!
So at this point you might go back to NVDA’s gaming sales, and ask the question: What happened in 2015? How is NVDA overcoming these secular trends?
The answer consists of a few parts.Firstly, AMD dropped the ball in 2015. As you can see in this chart, sourced from 3DCenter, AMD market share was halved in 2015, due to a particularly poor product line-up:
📷 https://hyperinflation2020.tumblr.com/private/618394753459994624/tumblr_J7vRw9y0QxMlfm6Xd
Following this, NVDA came out with Pascal in 2016 – a very powerful offering in the mid to high end part of the GPU market. At the same time, AMD was focusing on rebuilding and had no compelling mid or high end offerings. AMD mainly focused on maintaining scale in the very low end. Following that came 2017 and 2018: AMD’s offering was still very poor at the time, but cryptomining drove demand for GPUs to new levels, and AMD’s GPUs were more compelling from a price-performance standpoint for crypto mining initially, perversely leading to AMD gaining share. NVDA quickly remedied that by improving their drivers to better mine crypto, regaining their relative positioning, and profiting in a big way from the crypto boom. Supply that was calibrated to meet gaming demand collided with cryptomining demand and Average Selling Prices of GPUs shot through the roof. Cryptominers bought top of the line GPUs aggressively.
A good way to see changes in crypto demand for GPUs is the mining profitability of Ethereum:
📷 https://hyperinflation2020.tumblr.com/private/618394769378443264/tumblr_cmBtR9gm8T2NI9jmQ
This leads us to where we are today. 2019 saw gaming revenues drop for NVDA. Where are they likely to head?
The secular trends of falling desktop sales along with falling discrete GPU sales have reasserted themselves, as per the Jon Peddie research above. Cryptomining profitability has collapsed.
AMD has come out with a new architecture, NAVI, and the 5700XT – the first Iteration, competes effectively with NVDA in the mid-high end space on a price/performance basis. This is the first real competition from AMD since 2014.
NVDA can see all these trends, and they tried to respond. Firstly, with volumes clearly declining, and likely with a glut of second-hand GPUs that can make their way to gamers over time from the crypto space, NVDA decided to pursue a price over volume strategy. They released their most expensive set of GPUs by far in the latest Turing series. They added a new feature, Ray Tracing, by leveraging the Tensor Cores they had created for Professional uses, hoping to use that as justification for higher prices (more on this in the section on Professional GPUs). Unfortunately for NVDA, gamers have responded quite poorly to Ray Tracing – it caused performance issues, had poor support, poor adoption, and the visual improvements in most cases are not particularly noticeable or relevant.
The last recession led to gaming revenues falling 30%, despite NVDA being in a very strong position at the time vis-à-vis AMD – this time around their position is quickly slipping and it appears that the recession is going to be bigger. Additionally, the shift away from discrete GPUs in gaming continues.
To make matters worse for NVDA, AMD won the slots in both the New Xbox and the New PlayStation, coming out later this year. The performance of just the AMD GPU in those consoles looks to be competitive with NVidia products that currently retail for more than the entire console is likely to cost. Consider that usually you have to pair that NVidia GPU with a bunch of other expensive hardware. The pricing and margin impact of this console cycle on NVDA is likely to be very substantially negative.
It would be prudent to assume a greater than 30% fall in gaming revenues from the very elevated 2019 levels, with likely secular decline to follow.
The Professional Market:
A Bit of Ancient History (again, skip if impatient)
As it turns out, graphical accelerators were first used in the Professional market, long before they were employed for Gaming purposes. The big leader in the space was a company called Silicon Graphics, who sold workstations with custom silicon optimised for graphical processing. Their sales were only $25Mn in 1985, but by 1997 they were doing 3.6Bn in revenue – truly exponential growth. Unfortunately for them, from that point on, discrete GPUs took over, and their highly engineered, customised workstations looked exorbitantly expensive in comparison. Sales sank to 500mn by 2006 and, with no profits in sight, they ended up filing for bankruptcy in 2009. Competition is harsh in the semiconductor industry.
Initially, the Professional market centred on visualisation and design, but it has changed over time. There were a lot of players and lot of nuance, but I am going to focus on more recent times, as they are more relevant to NVidia.
Some More Modern History
NVDA’s Professional business started after its gaming business, but we don’t have revenue disclosures that show exactly when it became relevant. This is what we do have – going back to 2005:
📷 https://hyperinflation2020.tumblr.com/private/618394785029472256/tumblr_fEcYAzdstyh6tqIsI
In the beginning, Professional revenues were focused on the 3D visualisation end of the spectrum, with initial sales going into workstations that were edging out the customised builds made by Silicon Graphics. Fairly quickly, however, GPUs added more and more functionality and started to turn into general parallel data processors rather than being solely optimised towards graphical processing.
As this change took place, people in scientific computing noticed, and started using GPUs to accelerate scientific workloads that involve very parallel computation, such as matrix manipulation. This started at the workstation level, but by 2007 NVDA decided to make a new line-up of Tesla series cards specifically suited to scientific computing. The professional segment now have several points of focus:
  1. GPUs used in workstations for things such as CAD graphical processing (Quadro Line)
  2. GPUs used in workstations for computational workloads such as running engineering simulations (Quadro Line)
  3. GPUs used in workstations for machine learning applications (Quadro line.. but can use gaming cards as well for this)
  4. GPUs used by enterprise customers for high performance computing (such as modelling oil wells) (Tesla Line)
  5. GPUs used by enterprise customers for machine learning projects (Tesla Line)
  6. GPUs used by hyperscalers (mostly for machine learning projects) (Tesla Line)
In more recent times, given the expansion of the Tesla line, NVDA has broken up reporting into Professional Visualisation (Quadro Line) and Datacenter (Tesla Line). Here are the revenue splits since that reporting started:
📷 https://hyperinflation2020.tumblr.com/private/618394798232158208/tumblr_3AdufrCWUFwLgyQw2
📷 https://hyperinflation2020.tumblr.com/private/618394810632601600/tumblr_2jmajktuc0T78Juw7
It is worth stopping here and thinking about the huge increase in sales delivered by the Tesla line. The reason for this huge boom is the sudden increase in interest in numerical techniques for machine learning. Let’s go on a brief detour here to understand what machine learning is, because a lot of people want to hype it but not many want to tell you what it actually is. I have the misfortune of being very familiar with the industry, which prevented me from buying into the hype. Oops – sometimes it really sucks being educated.
What is Machine Learning?
At a very high level, machine learning is all about trying to get some sort of insight out of data. Most of the core techniques used in machine learning were developed a long time ago, in the 1950s and 1960s. The most common machine learning technique, which most people have heard of and may be vaguely familiar with, is called regression analysis. Regression analysis involves fitting a line through a bunch of datapoints. The most common type of regression analysis is called “Ordinary Least Squares” OLS regression, and that type of regression has a “closed form” solution, which means that there is a very simple calculation you can do to fit an OLS regression line to data.
As it happens, fitting a line through points is not only easy to do, it also tends to be the main machine learning technique that people want to use, because it is very intuitive. You can make good sense of what the data is telling you and can understand the machine learning model you are using. Obviously, regression analysis doesn’t require a GPU!
However, there is another consideration in machine learning: if you want to use a regression model, you still need a human to select the data that you want to fit the line through. Also, sometimes the relationship doesn’t look like a line, but rather it might look like a curve. In this case, you need a human to “transform” the data before you fit a line through it in order to make the relationship linear.
So people had another idea here: what if instead of getting a person to select the right data to analyse, and the right model to apply, you could just get a computer to do that? Of course the problem with that is that computers are really stupid. They have no preconceived notion of what data to use or what relationship would make sense, so what they do is TRY EVERYTHING! And everything involves trying a hell of a lot of stuff. And trying a hell of a lot of stuff, most of which is useless garbage, involves a huge amount of computation. People tried this for a while through to the 1980s, decided it was useless, and dropped it… until recently.
What changed? Well we have more data now, and we have a lot more computing power, so we figured lets have another go at it. As it happens, the premier technique for trying a hell of a lot of stuff (99.999% of which is garbage you throw away) is called “Deep Learning”. Deep learning is SUPER computationally intensive, and that computation happens to involve a lot of matrix multiplication. And guess what just happens to have been doing a lot of matrix multiplication? GPUs!
Here is a chart that, for obvious reasons, lines up extremely well with the boom in Tesla GPU sales:
📷 https://hyperinflation2020.tumblr.com/private/618394825774989312/tumblr_IZ3ayFDB0CsGdYVHW
Now we need to realise a few things here. Deep Learning is not some magic silver bullet. There are specific applications where it has proven very useful – primarily areas that have a very large number of very weak relationships between bits of data that sum up into strong relationships. An example of ones of those is Google Translate. On the other hand, in most analytical tasks, it is most useful to have an intuitive understanding of the data and to fit a simple and sensible model to it that is explainable. Deep learning models are not explainable in an intuitive manner. This is not only because they are complicated, but also because their scattershot technique of trying everything leaves a huge amount of garbage inside the model that cancels itself out when calculating the answer, but it is hard to see how it cancels itself out when stepping through it.
Given the quantum of hype on Deep learning and the space in general, many companies are using “Deep Learning”, “Machine Learning” and “AI” as marketing. Not many companies are actually generating significant amounts of tangible value from Deep Learning.
Back to the Competitive Picture
For the Tesla Segment
So NVDA happened to be in the right place at the right time to benefit from the Deep Learning hype. They happened to have a product ready to go and were able to charge a pretty penny for their product. But what happens as we proceed from here?
Firstly, it looks like the hype from Deep Learning has crested, which is not great from a future demand perspective. Not only that, but we really went from people having no GPUs, to people having GPUs. The next phase is people upgrading their old GPUs. It is much harder to sell an upgrade than to make the first sale.
Not only that, but GPUs are not the ideal manifestation of silicon for Deep Learning. NVDA themselves effectively admitted that with their latest iteration in the Datacentre, called Ampere. High Performance Computing, which was the initial use case for Tesla GPUs, was historically all about double precision floating point calculations (FP64). High precision calculations are required for simulations in aerospace/oil & gas/automotive.
NVDA basically sacrificed HPC and shifted further towards Deep Learning with Ampere, announced last Thursday. The FP64 performance of the A100 (the latest Ampere chip) increased a fairly pedestrian 24% from the V100, increasing from 7.8 to 9.7 TF. Not a surprise that NVDA lost El Capitan to AMD, given this shift away from a focus on HPC. Instead, NVDA jacked up their Tensor Cores (i.e. not the GPU cores) and focused very heavily on FP16 computation (a lot less precise than FP64). As it turns out, FP16 is precise enough for Deep Learning, and NVDA recognises that. The future industry standard is likely to be BFloat 16 – the format pioneered by Google, who lead in Deep Learning. Ampere now does 312 TF of BF16, which compares to the 420 TF of Google’s TPU V3 – Google’s Machine Learning specific processor. Not quite up to the 2018 board from Google, but getting better – if they cut out all of the Cuda cores and GPU functionality maybe they could get up to Google’s spec.
And indeed this is the problem for NVDA: when you make a GPU it has a large number of different use cases, and you provide a single product that meets all of these different use cases. That is a very hard thing to do, and explains why it has been difficult for competitors to muscle into the GPU space. On the other hand, when you are making a device that does one thing, such as deep learning, it is a much simpler thing to do. Google managed to do it with no GPU experience and is still ahead of NVDA. It is likely that Intel will be able to enter this space successfully, as they have widely signalled with the Xe.
There is of course the other large negative driver for Deep Learning, and that is the recession we are now in. Demand for GPU instances on Amazon has collapsed across the board, as evidenced by the fall in pricing. The below graph shows one example: this data is for renting out a single Tesla V100 GPU on AWS, which isthe typical thing to do in an early exploratory phase for a Deep Learning model:
📷 https://hyperinflation2020.tumblr.com/private/618396177958944768/tumblr_Q86inWdeCwgeakUvh
With Deep Learning not delivering near-term tangible results, it is the first thing being cut. On their most recent conference call, IBM noted weakness in their cognitive division (AI), and noted weaker sales of their power servers, which is the line that houses Enterprise GPU servers at IBM. Facebook cancelled their AI residencies for this year, and Google pushed theirs out. Even if NVDA can put in a good quarter due to their new product rollout (Ampere), the future is rapidly becoming a very stormy place.
For the Quadro segment
The Quadro segment has been a cash cow for a long time, generating dependable sales and solid margins. AMD just decided to rock the boat a bit. Sensing NVDA’s focus on Deep Learning, AMD seems to be focusing on HPC – the Radeon VII announced recently with a price point of $1899 takes aim at NVDAs most expensive Quadro, the GV100, priced at $8999. It does 6.5 TFLOPS of FP64 Double precision, whereas the GV100 does 7.4 – talk about shaking up a quiet segment.
Pulling things together
Let’s go back to what NVidia fundamentally does – paying their engineers to design chips, getting TSMC to print those chips, and getting board partners in Taiwan to turn them into the final product.
We have seen how a confluence of several pieces of extremely good fortune lined up to increase NVidia’s sales and profits tremendously: first on the Gaming side, weak competition from AMD until 2014, coupled with a great product in form of Pascal in 2016, followed by a huge crypto driven boom in 2017 and 2018, and on the Professional side, a sudden and unexpected increase in interest in Deep Learning driving Tesla demand from 2017-2019 sky high.
It is worth noting what these transient factors have done to margins. When unexpected good things happen to a chip company, sales go up a lot, but there are no costs associated with those sales. Strong demand means that you can sell each chip for a higher price, but no additional design work is required, and you still pay the printer, TSMC, the same amount of money. Consequently NVDA’s margins have gone up substantially: well above their 11.9% long term average to hit a peak of 33.2%, and more recently 26.5%:
📷 https://hyperinflation2020.tumblr.com/private/618396192166100992/tumblr_RiWaD0RLscq4midoP
The question is, what would be a sensible margin going forward? Obviously 33% operating margin would attract a wall of competition and get competed away, which is why they can only be temporary. However, NVidia has shifted to having a greater proportion of its sales coming from non-OEM, and has a greater proportion of its sales coming from Professional rather than gaming. As such, maybe one can be generous and say NVDA can earn an 18% average operating margin over the next cycle. We can sense check these margins, using Intel. Intel has a long term average EBIT margin of about 25%. Intel happens to actually print the chips as well, so they collect a bigger fraction of the final product that they sell. NVDA, since it only does the design aspect, can’t earn a higher EBIT margin than Intel on average over the long term.
Tesla sales have likely gone too far and will moderate from here – perhaps down to a still more than respectable $2bn per year. Gaming resumes the long-term slide in discrete GPUs, which will likely be replaced by integrated GPUs to a greater and greater extent over time. But let’s be generous and say it maintains $3.5 Bn Per year for the add in board, and let’s assume we keep getting $750mn odd of Nintendo Switch revenues(despite that product being past peak of cycle, with Nintendo themselves forecasting a sales decline). Let’s assume AMD struggles to make progress in Quadro, despite undercutting NVDA on price by 75%, with continued revenues at $1200. Add on the other 1.2Bn of Automotive, OEM and IP (I am not even counting the fact that car sales have collapsed and Automotive is likely to be down big), and we would end up with revenues of $8.65 Bn, at an average operating margin of 20% through the cycle that would have $1.75Bn of operating earnings power, and if I say that the recent Mellanox acquisition manages to earn enough to pay for all the interest on NVDAs debt, and I assume a tax rate of 15% we would have around $1.5Bn in Net income.
This company currently has a market capitalisation of $209 Bn. It blows my mind that it trades on 139x what I consider to be fairly generous earnings – earnings that NVidia never even got close to seeing before the confluence of good luck hit them. But what really stuns me is the fact that investors are actually willing to extrapolate this chain of unlikely and positive events into the future.
Shockingly, Intel has a market cap of 245Bn, only 40Bn more than NVDA, but Intel’s sales and profits are 7x higher. And while Intel is facing competition from AMD, it is much more likely to hold onto those sales and profits than NVDA is. These are absolutely stunning valuation disparities.
If I didn’t see NVDA’s price, and I started from first principles and tried to calculate a prudent price for the company I would have estimated a$1.5Bn normalised profit, maybe on a 20x multiple giving them the benefit of the doubt despite heading into a huge recession, and considering the fact that there is not much debt and the company is very well run. That would give you a market cap of $30Bn, and a share price of $49. And it is currently $339. Wow. Obviously I’m short here!
submitted by HyperInflation2020 to stocks [link] [comments]

Win Win improvements for Raid

We often get a lot of posts complaining about the cost to complete tournements and events, lack of goodies for FTP etc... but I gather those vent sessions like shouting into the wind. Plarium is a for-profit company, and they've chosen to run this game in a manner that works to maximize profits through whales.
With that said, I see a number of ways that Plarium can improve gameplay for users while also improving their revenue base, or improve game quality of life without imparing existing revenue. My thought is that the community would be better served to identify so-called "win\wins" and push for those specific improvements.
Here is my list:
QOL improvements that may lower quit rates
  1. roll out stage 20 for potions & minitaur - increased energy usage partially/largely offsets increased drop rates, but mid/end game players don't have to spend significant time running these dungeons.
  2. increase the number of EXP postions when dropped - Currently getting 1 is a slap in the face... 4-5 exp potions don't change game economics but will make players feel better.
  3. Remove tail risk from artifact upgrades - After X failed rolls to upgrade, your chances of success can slowly and very marginally start to increase. The average rolls for success can even be kept unchanged by marginally lowering success rates overall. The end result is to remove the risk of that 40 rolls to go from 15 to 16 that costs 3 million for a banner. I would guess that anyone that has experience a truely terrible set of bad luck rolls from level 15 to 16 on an accesory has thought about quitting. Some, including spenders, almost surely have quit.
  4. rearrange quests - covered ad naseum, but worth repeating. Maybe upcoming promised arena fixes address this, but all arena quests are significantly hard to achieve than other quests and represent an odd roadblock.
  5. Improve Gem production rates with gem mine - 5 with first upgrade, 15 with second, 30 with third (currently 5/10/15). Users will be more likely to invest gems (aka buy gers with $$$).
Revenue Generating Win/Wins for Plarium
Plarium's current business model is similar to a casino where the high roller area takes up about 80% of the real estate of the casino. This contrasts with the modern business model that; while still keenly focused on whales, also has a plethora of other offerings to the masses. Plarium needs to expand it's mass appearl offerings (please refrain from what about FTP players, plarium wants to make $$, lets accept it).
spending $20 a month should allow for more consistent game playing then it does now. $10 for the Raid card + $10 for the monthly gem package would get more consistent buyers if it facilitated more game play. Currently the gem package + gem mine + daily refills + random other refills (events, tournement, login, etc... ) rarely facilitate completing the 100 runs on auto... ergo the primary pass benefit is useless... also users instead use scripts to auto.
  1. half price gear removal once a month - users will probably waste more silver, since they will be more likely to de-gear and optimize.
  2. Raid Pass - buyers get reduced the time for energy replensish from 2 to 3 minutes. A modest 240 increase in energy per day, but a real benefit.
  3. Refills - Reduce refills from 40 to 30 gems - between gem mine, daily rewards, and the monthly gem pack, and misc refills, you now have enough energy daily to justify the raid pass autos, which will increase purchase rate of the raid pass.
  4. Greatly increase lower value rewards in tournements and events - Congrats, you wone 5,000 "units" at the slot machine... but it's a penny slot. The revenue model works. More rare skill tombs, more silver, more exp potions, more double exp, more mystery shards (with added feature for 100 myster shards = 1 Ancient shard), etc... Moderately increase other items such as ancient shards, void shards, epic books, etc... tl;dr - Make it so that FTP and low pay customers have plenty of goodies to win, even if they've no hope of getting everything/high valued items without saving.
  5. What about lost revenue from devauled goodies? - if smart, there won't be. Keep super high value items at current value/rarity. Whales will keep spending for sacred shards and legendary books. Introduce even higher value items to further entice whales... ie an "ideal shard" that gaurantees a legendary, or anchient void that gaurantees epic/legendary void.
submitted by hickhead00 to RaidShadowLegends [link] [comments]

A mini guide to Mardu Dredge in PD S18

A mini guide to Mardu Dredge in PD S18
A mini guide to Mardu Dredge in PD S18
I’ve been playing a lot of Penny Dreadfull this season and the deck I like the best so far is dredge. After having played it for years in modern, it's an archetype I’m very comfortable with. So when both Golgari Grave-Troll and Stinkweed Imp were confirmed legal I was excited to get to play with them. My first experiment with dredge was the Jund Squee dredge deck (see my deck tech video here). Later I saw some people having success with Mardu dredge and I decided I wanted to try that out as well.
After my first tries with copied lists I decided I wanted to streamline the Mardu list as much as possible. Gone is the cute stuff like dread return for sire of insanity, I’ve also gotten rid of the awkward Ashen Ghoul. The dread return package was seldom useful. Most common scenario was that you would dredge over one and not the other and then those cards are essentially blanks. You cannot reliably dredge over your entire deck fast enough each game and if you do, you don’t really need much more help. Ashen ghoul was a little better but also not consistent enough, in the face of graveyard hate it was also always the first card to side out (if your opponent is removing your graveyard it’s unlikely to ever have enough creatures on top of it).
I decided to fill the open slots with more enablers, to increase the consistency of the deck and your opening hands. Any opening hand that doesn’t contain an enabler + a dredger is a mulligan. If your enabler is a shriekhorn then you don’t need the dredger as the horn can try to find it for you. Most lists I saw were on 10 enablers. 4 shriekhorn, 4 cathartic reunion and 2 insolent neonate. From my experience with modern dredge I’d say you want at least 12. So I have added an extra Neonate and also a thrill of possibility. Thrill drawing 2 cards makes up for the fact that it’s a little slower as a 2 cmc card. If you discard a dredger and get lucky and find a second dredger on your first draw it can be quite powerful.
I also saw some lists that were only running 1 darkblast. I think 10 dredgers is the minimum amount of dredgers you want and I have actually considered going up to 11. Just to make the deck as consistent as possible. You want to keep the dredge chain going by dredging into dredgers. The more cards with dredge you run, the higher the chance of doing so. Darkblast only dredging 3 ultimately made it too weak for me but I could see changing my mind on this in the future.
After doing this I had one slot left for a fun off. I’ve decided to go with First-Sphere Gargantua. It can help you speed up when you don’t have any additional card draw anymore and you are just dredging once per turn. This card lets you draw a card from your graveyard and get an extra dredge in, on top of attacking for 5. With the heavy burn focus of the mardu deck, a lava axe from the graveyard is not bad. This is the list I decided on.

https://preview.redd.it/gthwyoglkiu51.png?width=1594&format=png&auto=webp&s=c7d099debaf0384d2b5ee9389ebdfd0246281a27
Opening hands
As mentioned above in your opening hands you are looking for an enabler (cathartic reunion, insolent neonate, thrill of possibility or shriekhorn) and a dredger. Again in the case of shriekhorn a dredger is not needed. With your 7 and 6 card hands you also really want that dredger to be either a troll or an imp as darkblast simply isn’t powerful enough.
If you have the above the next thing to consider is your mana. You want at least two lands and ideally those lands can make WR, Bx and RR. You need red to cast your enablers, white and red to be able to flashback smiting helix, black to be able to sac ghoul and return haunted dead (discarding dredgers) and finally RR to be able to flashback conflagrate. You can manage without double red and or black mana. But I’d say that RW is essential. Keep in mind that once you start dredging you ideally will not be taking any normal draw steps so the lands you have in your opener might be the only lands you’ll see for the entire game.
Don’t be afraid to mulligan with this deck. You can easily mull to 4 and still win. I’ve even mulled to 3 and won. If you keep a non functioning 5, 6 or 7 you are much more likely to lose.
So after you have settled on your opening hand you might need to put some cards on the bottom. I like keeping excess lands if possible as you are unlikely to draw any once you get going. I try to avoid bottoming Silversmote Ghoul as we don’t have any ways to shuffle our deck. So if the ghoul goes on the bottom, that’s one less ghoul to get dredge into and get back.
Keep track of what you’ve put on the bottom of your deck. If I put a creeping chill on the bottom I make a mental note of this so I can consider that when trying to calculate odds of hitting one.
Sideboarding
Another important part of playing dredge is sideboarding. Luckily it’s much easier to do than in other formats because the hate is less punishing when you get it wrong. You can still power through stuff like tormod’s crypt, unlike rest in peace or leyline of the void.
I’m not going to do a full sideboard guide for every matchup. There are too many decks to consider. I will give some general advice on what to board in and which cards you can take out.
First of all, don’t overboard. Your deck is a synergistic machine, if you take out to many pieces the machine stops functioning. Just because your opponent is playing some creatures doesn’t mean you need to board lightning axe to kill them. Often you don’t actually care about their creatures.
3 Deafening Silence: good vs storm and other spell based combo decks. Those are your weakest matchups so it’s fine to have these as narrow cards.
2 Duress: good vs various combo decks. Don’t board these in vs counterspells or stuff like cling to dust. It will slow you down too much and you will need to take out other cards to be able to fit in duress.
2 Lightning Axe: great against creature deck or combo decks relying on creatures. Lightning axe also lets you discard a card so you can consider this as an enabler and take out insolent neonate to fit this in.
3 Nihil Spellbomb: to fight the mirror and reanimator decks. I like this over tormod’s crypt as I value the card draw on it.
3 Fragmentize: you’ll board this in a lot as an answer to common graveyard hate like tormod’s crypt and nihil spellbomb. Again, don’t board this in just because your opponent is running things you could destroy only do so if you actually care about those cards.
1 Ray of Revelation: it’s a dregeable answer to enchantments. Great vs Jeskai ascendancy and animate dead and stuff like runed halo etc.
1 Shenanigans: a dredgeable answer to common hate cards. The nice thing about shenanigans is that if you dredge into it your opponent will often have to blow their graveyard hate right there. Otherwise you dredge it, blow up their hate piece, which will result in them using it and that will leave your shenanigans in the graveyard for any future graveyard hate cards. Obviously also great vs affinity.
What to cut? It’s important to keep the right balance of payoffs, dredgers and enablers. To me the below are cards you can trim:
2-3 insolent neonate
1 thrill of possibility
1-2 haunted dead
1-2 darkblasts
1 first-sphere gargantua
1 smiting helix
The rest of the cards I would not touch. Always try to cut a little bit of everything between dredgers, enablers and payoffs.
Tips and tricks
  • Remember that Silversmote ghoul can sacrifice itself to draw a card (and replace that with a dredge). This is useful in a couple of different ways. Firstly it’s good vs removal, especially removal that exiles. Secondly if you have already gained 3 life that turn you can sacrifice the ghoul, draw a card and dredge and you are guaranteed it will come back. Thirdly if you know you will not get another turn or your ghouls can no longer profitably attack you can use it to dredge and to look for burn to finish off your opponent.
  • Darkblast can kill 2 toughness creatures. If you have a darkblast in hand you can darkblast their creature on your upkeep, dredge the darkblast in your drawstep and then darkblast their creature again to finish it off. You do need two black mana for this but it can help you remove a flipped delver for example.
  • You often know exactly what is left in your deck. Sometimes you just need to deal the last points of damage and you know that if you dredge the last 2 chills in your deck you’ll win. Whereas if you draw one of them, you can’t cast it and you’ll lose. In those cases be conscious of which cards you are dredging and don’t just blindly go to dredge a Grave-Troll for 6 just to maximize your dredging for the turn. For example if you have 10 cards left in your library and you have 2 stinkweed imps and 2 grave-trolls in your graveyard you should be dredging the imps and not the trolls. If you dredge the troll you will not be able to dredge anymore that game and lose the chance of flipping chills if they are in the bottom 4 cards of the deck. Therefore you should dredge an imp twice and ensure you flip over your whole library.
  • Always have a stop on your upkeep. You often want to activate shriekhorn on your upkeep to look for a dredger. But also cast darkblast, lightning axe (discarding a dredger) or returning haunted dead (discarding dredgers).
  • Conflagrate can also be an enabler if needed. You can cast it for X = 0 and then flash it back, discarding dredgers to enable dredging them.
  • Stinkweed imp is an amazing blocker. It has deathtouch and can come back every turn. Some decks will have trouble attacking through this. Sometimes it’s better to dredge an imp over a troll and spend your turn and mana casting it.
  • It doesn't come up often but Golgari Grave-Troll can be cast and can actually be a sizable threat if you have a stocked graveyard. His regenerate ability also makes him really sticky. Alternatively you can also cast a Grave-Troll when you graveyard has no creatures in it, it will die and you will have a dredger in your graveyard again.
  • When you are facing graveyard hate and you don’t have removal for it it’s often correct to keep dredging and force them to use their hate card. However you don’t want to go to all in and lose all your cards forever. You want to create a situation where you graveyard is just threatening enough to force your opponent to react. This is also a real skill tester for your opponent when to use their hate. There’s a decent chance they get it wrong and you can capitalize on that.
Hope you found this mini primer informative. I will be putting out a league recording in the coming days so you can see me pilot the deck and put all this theory into practice. If you ever have any questions about this deck or any others you see me play, feel free to reach out to me on MTGO or Discord.
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what are the odds of winning on a penny slot machine

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