A Chess Game?
Many fans and analysts liken American football to a game of chess. Each team strives to stay one step ahead of opponents through study of game films, opponent-specific playcalls, and in-game strategy adjustments. Given the comparison to chess, if IBM’s Deep Blue computer was able to outmaneuver a chess grandmaster, there must be an opportunity for machine learning to improve the playcalling process for the National Football League (NFL).
Before each football play takes place, coaches have a staggering number of ways to combine the personnel (which players are on the field), with formation (where players line up before the play starts), and concept (assignments for each of the eleven players once the play begins). Each playcall decision is selected to put the team’s players in the best position for success. During games, teams have 40 seconds for coaches to make the playcall decision, relay that information to players on the field, and for players to line up and begin the play. The current playcall process can be significantly improved through machine learning capabilities.
The ideal playcall varies depending on many factors including down, distance, ball location on the field, game time remaining, and the opponent’s playcall. Currently, many coaches refer to a “playsheet,” which categorizes preferred playcalls based on the ideal situations to choose them. Because of the 40 second time constraint, these playsheets contain only a small subset of possible playcalls and are not large enough to address every possible game situation. A machine learning algorithm that can rapidly provide an assortment of recommended playcalls based on the current game situation and the opponent’s expected playcall has the potential to greatly improve team performance.
In 2014, the NFL began a program called “Next Gen Stats,” aimed at uncovering deeper insights into on-field action, and improving the fan experience by providing a broader range of advanced statistics. This program is tracking the data necessary to enhance in-game playcalling strategies, along with many other aspects of team operations such as training, fitness, and preparation. Notable developments include adding location-tracking technology into players’ equipment and game balls, a partnership with Amazon Web Services to host and analyze the 3TB of data generated from NFL games each week, and a proprietary Next Gen Stats website for fans to learn about the league’s evolving data analysis capabilities.
With respect to the use of machine learning for playcalling, the NFL currently provides each team with just its own data, and not that of its opponents. While many hope to eventually make all teams’ data available league-wide, the final decision lies with the NFL Competition Committee. With data on one’s own team, a coach can determine if the team has shown any predictable tendencies or weaknesses that may be exploited by an opponent, but will not have data on opponents to help strategize for upcoming matchups or recommend situational playcalls. In the past, the NFL has made enhancements to the playcalling process by adding speakers to players’ helmets for live communication from coaches, and providing on-field tablets to let teams review plays from earlier in the game.
Given the NFL’s history of process improvement around playcalling, adding a machine learning recommendation capability would be a logical next step. This capability will allow teams to make more informed playcall selections and potentially increase the pace of the game, which fans would enjoy. One important factor to consider when utilizing machine learning recommendations is which players are on the field. For example, a certain play may have a very high success rate because it leverages the ability of one of the team’s most talented players; if this player is not on the field when that play is recommended, the likelihood of success may be significantly overestimated. Another consideration is how to weigh the importance of a playcall’s previous success against predictability. A machine learning algorithm will become more confident in a playcall that has been successful in the past, but eventually an opponent will markedly alter their strategy to defend against that playcall. It would be difficult to program a machine learning algorithm to foresee when that opponent’s adjustment will happen in real time and preemptively counter with a less predictable playcall.
Lastly, an important question this topic brings to light is related to the competitive nature of football. In the end, the NFL is a form of entertainment, and its major organizational decisions focus on whether they are improving the game for fans. Does a playcalling algorithm simply add another tool for coaches to use to make the league more competitive, or could automation lead to complacency by reducing the need for preparation and ultimately undercutting the coaching prowess that so many football fans currently admire?
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 More information on the NFL Competition Committee available at https://operations.nfl.com/football-ops/league-governance/the-nfl-competition-committee/.
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