An Editor’s Cut on Sports Analytics edited by Scott Nestler and Anne Robinson is available. The volume is a collection of sports analytics articles published in INFORMS journals. Some of the articles are free to download for a limited time if you don’t have a subscription. But there is more than academic papers in the Editor’s Cut.
Here are some of my favorite articles from the volume.
Technical Note—Operations Research on Football [pdf] by Virgil Carter and Robert E. Machol, 1971. This is my favorite. This article may be the first sports analytics paper ever and it was written in an operations research journal (w00t!). It’s written by an NFL player who used data to estimate the “value” of field position and down by watching games on film and jotting down statistics. For example, first and 10 on your opponent’s 15 yard line is worth 4.572 expected points, whereas first and 10 on your 15 yard line is worth -0.673 expected points. This idea is used widely in sports analytics and by ESPN’s Analytics team to figure out things like win probabilities. This paper was way ahead of its time. You can listen to a podcast with Virgil Carter here (it’s my favorite sports analytics podcast).
An Analysis of a Strategic Decision in the Sport of Curling by Keith A. Willoughby and Kent J. Kostuk, 2005. This is a neat paper. I have never curled but can appreciate the strategy selection at the end of a game. In curling, the choice is between taking a single point or blanking an end in the latter stages of a game. Willoughby and Kostuk use decision trees to evaluate the benefits and drawbacks associated with each strategy. Their conclusion is that blanking the end is the better alternative. However, North American curlers make the optimal strategy choice whereas European curlers often choose the single point.
Scheduling Major League Baseball Umpires and the Traveling Umpire Problem by Michael A. Trick, Hakan Yildiz, Tallys Yunes, 2011. This paper develops a new network optimization model for scheduling Major League Baseball umpires .The goal is to minimize the umpire travel of the umpires, but league rules are at odds with this. Rules require each umpire to umpire for all the teams but not two series in a row. As a result, umpires typically travel more than 35,000 miles per season without having a “home base” during the season. The work here helps meet the league goals while making life better for the crew.
A Markov Chain Approach to Baseball by Bruce Bukiet, Elliotte Rusty Harold, José Luis Palacios, 1997. This paper develops and fits a Markov Chain to baseball (You had me at Markov chains!). The model is then used to do a number of different things such as optimize the lineup and forecast run distributions. They find that the optimal position for the “slugger” is not to bat fourth and for the pitcher to not bat last, despite most teams making these decisions.
The Loser’s Curse: Decision Making and Market Efficiency in the National Football League Draft by Cade Massey, Richard H. Thaler, 2013. Do National League Football teams overvalue the top players picked early in the draft? The answer: Yes, by a wide margin.
There are a couple of dozen papers that examine topics such as decision-making within a game, recruitment and retention issues (e.g., draft preparation), bias in refereeing, and the identification of top players and their contributions. Check it out.
The Editor’s Cut isn’t just a collection of articles. There are videos, podcasts, and industry articles. A podcast with Sheldon Jacobson is included in the collection. In it, Sheldon talks about bracketology, March Madness, and the quest for the perfect bracket:
A TED talk by Rajiv Maheswaran on YouTube is included in the collection (below) called “The Math Behind Basketball’s Wildest Moves.” It’s a description of how to use analytics to recognize what is happening on a basketball court at any given time using machine learning (is that a pick and roll or not?)
Other sports tidbits from around the web:
- Several people asked me whether Green Bay should have gone for a two-point conversation in their game against Arizona (they didn’t and lost in overtime). I thought it was an obvious decision. I didn’t write a post but Benjamin Morris on FiveThirtyEight covers this issue in detail.
- That course you had on graph theory is really useful. It explains why did the Patriots and the Panthers beat up on the same weak teams in the 2015 season.
Read the previous INFORMS Editor’s Cut on healthcare analytics.
Here are a few football analytics posts on Punk Rock OR:
- Punk Rock OR was on the Advanced Football Analytics podcast
- Should a football team run or pass? A game theory and linear programming approach
- Why the Patriots’ decision to let the Giants score a touchdown makes sense
- underpowered statistical tests and the myth of the hot hand
- Major League Baseball scheduling
- Introducing Badger Bracketology 1.0
- Some thoughts on the College Football Playoff
- Will the New York Times Fourth Down Robot change football?
- The NFL football draft and the knapsack problem
Who do you think will win the Superbowl? The Carolina Panthers or the Denver Broncos? Did you make this decision based on analytics?