In the sport of hockey, we often value players that are more physical, especially those that additionally produce points. In this week’s My Model Monday, I explored the importance of hits on NHL hockey games. Continue reading My Model Monday: Understanding the Impact of Hits in the NHL
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 basketball-reference.com. Continue reading My Model Monday: Deep Dive Into Technical Fouls
With the NBA and NHL playoffs in full swing, it gives us a good chance to look at which teams over/underachieved during the regular season using Pythagorean Win Expectation, and, in turn, what teams could exceed expectations in the playoffs. Continue reading My Model Monday: NBA & NHL Pythagorean Wins
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.
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.
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 NBADraft.net. Continue reading My Model Monday: NBA Draft Scouting Text Analysis
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
The first weekend of March Madness 2018 is in the books, and it was wild. When all was said and done, the same number of 11-seeds made the Sweet 16 as 1-seeds. Half of teams seeded 1-3 were knocked out in these first two rounds. And of course, the tournament saw a 1-seed exit in the first round for the first time ever (at least in the men’s tournament). It’s hard to imagine anyone’s bracket coming out of a weekend like this looking good. How bad was it? I dug into Model 284’s own March Madness contest to find out.
To use the Combine Tool, enter the name of a player and click go. The charts display that player’s scores in the 6 main NFL Combine drills: 40-yard dash, Broad Jump, Vertical Jump, Shuttle Run, 3 Cone Run, and Bench Press. The Yellow distribution curves show the distribution of other players’ scores from the selected player’s position. The numbers above each chart are the given player’s score for the drill and the percentile that that score falls in relative to other players at that position. The data comes from www.pro-football-reference.com and nflcombineresults.com. Our sample consists of all NFL Combine Invitees from 2005 – 2018. Look up some current and former players, or some players that will be coming off the board in this year’s draft!
Continue reading NFL Combine Tool
In sports, people love to categorize players by their playing style. For example, in hockey, people distinguish defensemen as offensive or defensive, or the rare all-around defensemen. In this week’s installment of My Model Monday, I look to create mathematical groupings of NHL defensemen using 2017-2018 NHL data.