Category Archives: My Model Monday

My Model Monday: Hockey Aging Curves

In this week’s My Model Monday, I explored aging curves in not just the NHL, but in other professional leagues and junior hockey leagues. First and foremost, what are aging curves? Aging curves are just what they sound like: curves that associate player performance and health over time. For a point of reference, despite his mighty accomplishments at an old age, Jaromir Jagr saw his point production dip from over a point per game at age 25 to about 0.3 PTS/G at age 45. Jaromir Jagr is an incredibly interesting case and likely outlier, as few players have played into their mid-forties. At any rate, one can imagine that many players experience similar increases and decreases in production with age; therefore, using many samples of players, one can construct a curve that resembles a mean of all players aging, or, as we like to say in the reinsurance industry, an industry exposure curve. Continue reading My Model Monday: Hockey Aging Curves

My Model Monday: Modeling NFL Injuries

Injuries are inevitable in a game as physical as NFL football. Every season, numerous star players and important contributors are sidelined, leaving their fans and fantasy owners disappointed. Injuries appear to strike at random; an elite athlete can have his knee blown out in one cut like Dalvin Cook last season. However, is there a way to identify if certain players are more injury prone than others? I dug into the data to find out how well we can predict injuries among skill position players in the NFL.
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My Model Monday: NFL Coaches Exceeding Win Totals

Every year in the barren wasteland of the NFL offseason, pre-season win totals are set by oddsmakers for each team, serving as a rough expectation for how many games a given team will win in the coming season (e.g., the New England Patriots currently have a win total of 11 for 2018). I took a look at how coaches have performed against those pre-season expectations, with the idea being that a “successful” season is one in which they exceeded their expected wins. As an example, the 2017┬áLos Angeles Rams had a pre-season win total of 6, but ended up winning 11 games, so that gives a +5 mark for 1st-year head coach Sean McVay. For coaches with multiple seasons under their belt, we can do that calculation for every season and roll-up the results to give a view of how a given coach has performed against expectations over a long period of time.
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My Model Monday: Deep Dive Into Technical Fouls

Tensions between players and NBA officials have been on the rise over the past couple years. In the 2016 – 2017 season, referees called 917 technical fouls, and in the 2017 – 2018 season that number increased to 946. Players and coaches are antagonizing referees more and more, forcing referees into handing out more technical fouls. On most occasions, a technical fouls is due to a player or coach letting their emotions get the best of them. Other times, the technical may have been drawn intentionally to send a message. When a technical foul is called, the opposing team is awarded a free throw, but the overall effect on the game doesn’t always end there. In this article, I investigate how technical fouls effect the game from the perspective of the teams involved and the officials. All of the data in this analysis came from Continue reading My Model Monday: Deep Dive Into Technical Fouls

My Model Monday: Visualizing Hitter Performance

All-encompassing stats are great for many things. WAR has become the standard for comparing baseball players because, more than any other stat, it encapsulates all of a player’s contributions in one number. On the offensive side, stats like OPS and wOBA provide a single stat that basically amount to offensive production per plate appearance. Stats like these are useful both in that they assign values to the different ways in which players provide value, and because universal measures allow easy comparisons between players. Want to know whether Giancarlo Stanton or Joey Votto was a more productive hitter last year? No number will give a perfect answer, but wOBA will get you close.

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My Model Monday: Wide Receiver Draft Model

This year’s class of players entering the NFL draft lacks real star power at the Wide Receiver position. At first glance, there doesn’t appear to be a cant-miss player like a Julio Jones or Calvin Jonson. However, it’s important to remember that the most dominant receiver in the game today, Antonio Brown, was considered a below average prospect prior to the 2010 draft, and wasn’t picked until the 6th round, after 21 (!) wide receivers already went off the board. It is certainly possible there is another hidden gem hiding in this years crop of Wide Receivers.

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My Model Monday: NBA Draft Scouting Text Analysis

With March Madness just wrapping up, the natural next step in the basketball calendar is to turn to the NBA Draft. At Model 284, we have created a number of different models projecting college basketball players in the NBA; these include: our Peak NBA Statline Projection (PNSP) which attempts to predict overall NBA ability on a scale of 0-100, Similarity Scores that capture style of play, and lastly, a Role Probability Model which puts a probability that each player ends up in a certain role in the NBA (All-Star, Starter/Sixth Man, Bench, or out of the league). One idea which has come up repeatedly in our draft analysis is incorporating scouting or subjective analysis into our draft models. Rather than using a rubric of someone’s scouting grades, I wanted to do a text analysis of scouting reports already written. Since DraftExpress recently folded and moved to the dark side with ESPN Insider (probably a smart business decision), I used Continue reading My Model Monday: NBA Draft Scouting Text Analysis

My Model Monday: Biggest March Madness Upsets

This year’s NCAA tournament brought some crazy upsets, including the first-ever 9 vs. 11 Elite Eight matchup and first ever 16 seed to win a game, which is being talked about as one of the biggest upsets in sports history. In today’s My Model Monday, I see if the numbers back up that claim, or if there are other dogs that give UMBC a run for their money, and see if there is anything we can learn from the teams that won. Continue reading My Model Monday: Biggest March Madness Upsets