11/18/2023 0 Comments Nfl play by play data 2020![]() ![]() A similar scenario is true with event data, where the dataset available to NFL analysts will be a multiple of the number of observations you are producing through the notation of events from a maximum of 40,000 plays per season.Ī very different scenario occurs with player tracking data, where the sample size is substantially larger. This means that for the longest time in the sport, analysts have had a maximum of almost 40,000 plays per season to figure out the answers to NFL analytics questions. When it comes to play data, there is an average of 155 plays per game, and 256 games played in a single season. The sample sizes of data that is available for NFL analysts to come up with new metrics varies for each one of these data types. Player tracking data was only started to be shared with teams from the 2016 season onwards. This is the most novel type of data being collected by the NFL. It tracks every player during every play of every game. It is usually captured at 10fps using radio frequency identification (RFID) chips located on each player’s shoulder pads as well as the ball. ![]() This type of data refers to 2-D player location data that provides the xy coordinates as well as the speed and direction of players. These companies tag events using video analysis software and collect data points such as the offensive formation, number of defenders in the box, defenders closer to the line of scrimmage, whether a cover scheme was man versus zone, the run play called and so on. It is usually performed by organisations such as Pro Football Focus or Sports Info Solutions by leveraging their football expertise. This data is generated from notating video footage. It also includes some outcome variables like number of yards gained, passer rating to evaluate QBs, win probability and expected points. This data contains the largest amounts of historical records and includes variables like the down, distance, yard line, time on the clock, participating teams, number of time outs and more. Each one of these types of data present different levels of complexity, with some having been around for longer than others. There are three types of data being collected and used by the NFL analytics teams: play level data, event level data and tracking level data. But how could data be applied to this play to tell a similar story? To do so, NFL analysts first needed to take a look at the data and information that was being collected from that play, to understand what was available to them and the structure of the datasets that will allow them to come up with possible uses for that data. ![]() An initial eye test by simply looking at the video footage told the analysts that in this particular play Zeke Elliot - the ball carrier - had a significant amount of space in front of him to pick up those 11 yards. Statisticians at the NFL then tried to understand what can be learned from a play like this one by breaking down the play to obtain as many insights on the teams involved, the offence, the defence, and even the ball carrier. This run by Zeke Elliot eventually allowed Dallas to successfully move further down the field and score points. To figure this out, they looked at a single example of a running play in a 2017 season game between Dallas and Kansas where the running back, Ezekiel ‘Zeke’ Elliot took 11 yards from a 3rd down and 1 yard-to-go. The first step that the NFL Football Operations team took to figure out what should be answered with the use of data is to try to understand what the general public thinks about when they watch an NFL game. What To Analyse With The Data Available In The NFL? In his talk, Michael walked through the journey that the NFL took to develop expected rushing yards, a concept that began as an initial idea within their Football Operations group and ended up making its way up to the NFL’s Next Gen Stats Group and the media. Michael Lopez, the Director of Data and Analytics at the NFL, recently discussed at the FC Barcelona Sports Tomorrow conference the way that his Football Operations team and the wider NFL analytics teams leverage a large community of NFL data enthusiasts to obtain a better understanding of the game of American Football. ![]()
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