January Minute Risers: Players That Have Seen The Biggest Increase in Playing Time

Jan 07

January Minute Risers: Players That Have Seen The Biggest Increase in Playing Time

I wrote an article two days ago about research that I did on statistics that have the highest correlation to overall value for Fantasy Basketball. In that article I was able to conclude that minutes played and, to a lesser extent, turnovers have the highest correlation with overall value, and I came up with a linear regression based off of turnovers and minutes played which came up with an expected player rater value (xPR/GM). While xPR/GM may not be more than another fun statistic to look at, because it fails to account for players that have been injured and/or have taken a on new role within their team and lack the overall minutes, the main takeaway from the article is that minutes played is the secondary statistic (i.e. not a statistic that contributes directly to one of the eight categories) that correlates the most with Fantasy Basketball value, at least for roto leagues. Taken further, minutes played has the highest correlation with overall Fantasy value, but if we only look at total minutes played, that doesn’t allow us as Fantasy Basketball owners to make trades or pick ups based on recent trends when some players have played more games than others. For that reason, minutes per game is a better indicator for changes in playing time and potential...

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What Has The Highest Correlation to Player Rater Score?

Jan 06

What Has The Highest Correlation to Player Rater Score?

My initial thought was to begin this article with the traditional sob story that every last place, self-absorbed fantasy owner sings; “Everyone on my team got hurt, all my stars underperformed, and I played every team in my league on their best week.” While all of this may be true for my 12 team head-to-head categories league team, I pretense this article with the statement above, not to gain sympathy, but to give context to the research that resulted in this article. In this article I show what metrics have the highest correlation with the fantasy basketball player rater score and propose a linear regression that looks at an expected player rater score based off of the variables with the highest correlations. All of the references to player rater scores in this article refer to the player rater scores that are produced by ESPN Fantasy Basketball for 12 team head to head category leagues that use these categories: points, rebounds, three pointers, field goal percentage, free throw percentage, blocks, and steals. The population for this study looks at player rater scores from the 2015 season so far. So while the sample size is very small, it gives us a general look at what metrics, outside of the metrics that are categories themselves, correlate highly to player rater scores. I was also unable to find a centralized database that archived historical player rater data, or any value metric in general, for fantasy basketball. I also didn’t have the time to go through each season from the last decade, create z scores for each category at each position, and create the player rater scores myself. But if anyone knows where I can get my hands on this information, I would be happy to do a more conclusive study. We also don’t want to look at player rater scores specifically; we want to look at player rater score per game. This allows for us to give credit to players like Russell Westbrook and Chris Bosh, who have been absent from play for an appreciable duration of the season, but have played exceptionally well while they’ve been on the court. Bellow are the metrics that I looked at and their correlation to player...

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Completion Percentage Is Flawed

Dec 29

Completion Percentage Is Flawed

As I watch Oklahoma face off against Clemson in the Russel Athletic Bowl (it feels wrong to say the name of a sponsor), and face off is a misnomer in this case due to the lack of competitiveness so far, I see Cole Stoudt, the Clemson quarterback, complete wide receiver screens ad nauseam against the Oklahoma defense. In this particular case, Oklahoma’s defensive backs can’t make the adjustments at the line of scrimmage to be able to neutralize the Clemson wide receivers. In other cases in general, the deliberate use of screen passes, dump offs to half backs, and check downs are used as more out of necessity, due to the limitations of a team’s quarterback, than as a strategic move. Anyone who saw the Dolphins and Ryan Tannehill play this year could have had the same sentiments after they watched the cautious approach the Dolphins took with their young signal caller and then looked at his numbers at the end of the day. Tannehill was able to conclude the season with the league’s fifth ranked completion percentage—all stats given in this article will be based off of quarterbacks that took fifty percent of their team’s snaps (27 in total)—but finished 21st in average yards per attempt (6.88 YPA) as the Dolphins took a judicious approach with their young talent. This isn’t to say that Tannehill didn’t have a good year, or that he won’t eventually develop into a good quarterback. It just points out one of the ways that completion percentage is flawed. This also isn’t to suggest that the aforementioned statement is ground breaking news, but, as the season comes to a close, I thought we could use this as a prompt to reflect on ways that we can improve completion percentage. Awards will be given out soon, and I’m not sure if these people are common or if the few people that I have heard convey this sentiment have just stuck in my head, but some have clamored for the recognition, if they’ve felt compelled enough to support his candidacy with such vigor, of Tony Romo and the possibility that he could be 2014 NFL MVP. These advocates point to Romo’s completion percentage: 1st in the NFL....

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Josh McCown & How He Might Ruin Doug Martin, Vincent Jackson, and Mike Evans

Sep 10

Josh McCown & How He Might Ruin Doug Martin, Vincent Jackson, and Mike Evans

Doug Martin, Vincent Jackson, and Mike Evans: these are the players on the Buccaneers that most Fantasy owners thought they could count on, but what did most owners not count on? Josh McCown. McCown was bad in his first regular season start with the Buccaneers. So bad that the two-year, $10 million dollar contract he signed in the offseason already looks reprehensible. How did Josh McCown get here? In his thirteen year NFL career, McCown has played with nine organizations, across two countries, and finally got his first opportunity to start in 2014 with the Buccaneers. McCown finally produced numbers that suggested he could look like an NFL starting back in 2013. However, one must take the context from which those numbers were produced, and my guess is that the Buccaneers have now realized a little too late how much context matters. For five games Marc Trestman made McCown a star. In those five games, McCown averaged 8.2 yards per pass—which is more than Peyton Manning, Drew Brees, and Matthew Stafford averaged in 2013—and was fourth in DVOA amongst QBs—higher than Drew Brees, Aaron Rodgers, and Russel Wilson. I could feel the eyes roll after that last sentence, and how could you not after performance that McCown put-on on Sunday. Buccaneers vs. Bears Thought Experiment While McCown may not have the same quality of skill position players he had in Chicago, overall, the situation still may be considered more advantageous. Lets think about this as a thought experiment. Say for some reason the Phil Emery lost his mind and decided to trade Jay Cutler in a one for one trade for Josh McCown, or that Jay Cutler was magically cloned and there were now two Jay Cutlers—there would really be three Jay Cutlers in this hypothetical scenario, because everyone knows who the real Jay Cutler is to begin with. Which team would win more games, the Bears with Jay Cutler or, as they are currently constructed, the Buccaneers with Jay Cutler? Sure, the Bears have Brandon Marshall and Alshon Jeffery, but the Buccaneers aren’t exactly barren of talent with Vincent Jackson and Mike Evans at the skill positions. In 2012 Jackson lead the NFL in yards per catch—19.2 yards—and...

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Does Troy Tulowitzki suffer without Carlos Gonzalez?

Aug 10

Does Troy Tulowitzki suffer without Carlos Gonzalez?

Does Troy Tulowitzki suffer without Carlos Gonzalez in the lineup? Several weeks ago, in the same way my last article on rookie first and second half splits was inspired, my attention was alerted when a podcast personality contrived that Troy Tulowitzki, before his most recent bout with the injury bug, had preformed poorly because Carlos Gonzalez had been out of the lineup. The pundit grabbed the lowest handing fruit he could find in an effort to create a narrative, and a dogmatic one at that, as to why the Colorado Rockies slugger had not lived up to his pre All-Star break numbers. ******* *******’s (I’d prefer the article to be more about the subject of Tulowitzki and Gonzalez than the podcast member) argument was that without Carlos Gonzalez in the lineup, pitchers could approach Tulowitzki without fear, give him less strikes, and that is why his hitting has declined. While this pundit surmised that Troy Tulowitzki’s performance declines when Carlos Gonzalez is out of the lineup, the numbers tell a much different story. Click here to read more by articles by Devin Jordan. While we will look at the more direct numbers in a moment, the idea that Tulowitzki plays worse without Gonzalez is essentially the idea of lineup protection at a micro level. There have been countless instances that have debunked the idea of lineup protection, and, to my knowledge, none that have proved its existence.   The research looked at all games from 2010—Carlos Gonzalez’ first complete season—to today. The results paint a much lighter picture than the Guernica that ******* ******* painted. In games where Tulo has played without Cargo, he has had a higher AVG, OBP, OPS, and BB%. One might think that Tulowitzki would continue his normal performance without Carlos Gonzalez in the lineup, but, as this information suggests, it is hard to imagine that Tulo plays better because Carlos Gonzalez is not in the lineup, which leads me to believe what one would normally think about out of the ordinary performances in a small amount of at bats. The utility of these results should be used for descriptive, and not predictive, purposes. Troy Tulowitzki has only had 479 plate appearances without Carlos Gonzalez,...

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Do Rookie Hitters Decline in the Second Half?

Jul 20

Do Rookie Hitters Decline in the Second Half?

Do rookies perform worse after the All-Star break? My claim over this statement is nonexistent, while the original thought of its occurrence was brought to my attention by Adam Aizer on the CBS Fantasy Baseball Podcast. My judgment dissuaded, I thought that it would be worth the effort to look into the validity of the statement. From the perspective of an offensive player, rookies infrequently make enough of an impact in the size of leagues (i.e. 10-team and 12-team leagues) that pedestrian Fantasy Baseball players occupy. For those sizes of leagues that the aforementioned owners participate in, a rookie hitter that is worth owning is either an elite prospect or a player that has preformed beyond their true talent level. As a result, the former is rare, while it would make sense for the latter to regress to their true talent level and is more common than the former. The idea that rookie hitters decline throughout the year is just a misevaluation of the player’s true talent level. To put another way, it is the same logic that comes into play with a recent event: the Home Run Derby. Players that participate in the Home Run Derby are players that have exceptional first halves, which are often beyond their true talent level. These players often perform worse in the second half than they did in the first half, not because they participated in the monotonous and dated event that has become the Home Run Derby, but because, just like the rookies who perform worse in the second half of the season than the first, they have regressed toward their true talent level; when the rookies regress, they have just regressed to the point where they are not ownable. The research looks at all player seasons between 1988 and 2013 where a batter was in their first season, had 250 plate appearances in the first half of the season, and had 250 plate appearances in the second half of the season. The rookie second half decline and the post Home Run Derby slump intuitively make sense, but intuition does not always bear truth. Through cognitive ease we rationalize that “Swinging that hard for that long throws off your timing”; “A...

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Don’t Pick Up Collin McHugh

May 29

Don’t Pick Up Collin McHugh

Collin McHugh has given us many reasons to believe that he is a fundamentally different pitcher than he was in the first two years of his career, but there is one reason that explains why McHugh has performed so well this year and suggests, “Don’t pick up Collin McHugh.” The curveball has been a source of impetus for McHugh. McHugh used his curveball 15.19% of the time in 2013, but has increased his usage rate of the breaking ball by 56%—he’s thrown it 23.7% of the time in 2014. By itself, an increase in usage doesn’t mean much, because if a pitcher starts to use a pitch more, and it was an ineffective pitch to begin with, he’s just chosen to use a bad pitch more often, and that wouldn’t suggest that the pitcher, unlike McHugh, would become a better pitcher. However, McHugh’s curveball has become a better pitch for him. Last year, the curveball managed to get hitters to swing and miss 12.31% of the time, which is still above the league average swSTR% of 11.1% on curveballs, but McHugh has managed to get whiffs on his curveball 19.51% of the time this year. To put what that 19.51% whiff rate at a 23.7% usage rate in perspective, when you look at the runs above average leaders, per pitch type, for 2014, McHugh’s curveball has been the fifth most valuable curveball for all starting pitchers. McHugh has not only used his curveball more, but his curveball has been more effective, which makes one ask, “Why has it been so much better?” The average velocity on his curveball has gone up from 71 MPH last year to 73 MPH this year, but that still doesn’t explain the drastic increase in his swSTR%; slow curveballs—pitches that are around 72 MPH—on average, get an 11% swSTR%, while fast curveballs (think about what Justin Verlanders’ curve used to be like)—pitches around 81.5 MPH—get, on average, a 14% swSTR%. If velocity isn’t the reason that McHugh’s swSTR% has gone up, maybe he’s gotten better movement on the pitch?   To answer that question quickly, the vertical movement on his fastball has increased but not enough to be statistically significant or explain why his whiff...

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