9/1/2023 0 Comments Nfl play by play data 2020![]() ![]() Expected Points Added (Pass Plays vs. Running Plays)Īs shown in the following table, on average in 2019, passing plays gained 0.07 EPA/play, while running plays to the outside lost 0.01 EPA/play, and running plays up the middle lost 0.06 EPA/play. Also, because quarterback scrambles are often intended to be pass plays, scrambles were redefined as “pass plays.” Running plays having a run_location=NA are not included in the data set, as these plays are generally aborted plays such as fumbles by the quarterback at the snap. Running plays toward the run_gap of tackle or end were classified as “outside runs.” “Middle runs” include runs having a run_location of middle or a run_gap of guard. To confirm and apply these previous findings, the data was split into three types of plays: outside runs, middle runs, and pass plays. Similarly, in Big, Useful Data Says Outside Rushes are the New Market Inefficiency, Dan Morse shows that rushing outside has been slightly more successful than rushing up the middle, except for short yardage situations (less than 5 yards to go). In The NFL’s Run Gap Secret, Tej Seth shows that runs outside the tackles result in higher EPAs than inside runs. Recent research has examined which run play locations have been most successful. The nflscrapR data provides run_location and run_gap information that allows for an evaluation of running play success by run direction. Also, 2-point conversion attempts (which have a down=NA), and any plays that have NAs for epa or wp were removed from the data set. The data was filtered by play_type so that only pass plays and run plays were included. The play_type is blank for rows indicating the end of play. Other play_type indicators include: punt, field_goal, kickoff, extra_point, qb_kneel, qb_spike, and no_play (timeouts and penalties). The play_type attribute classifies each play as a pass (including sacks) or a run (including scrambles). wp (Win Probability for team with possession at start of play)ĭescriptions of all attributes in the nflscrapR play-by-play data set are available here.epa (Expected Points Added for team with possession).Specifically, Ron Yurko provides play-by-play data for all regular seasons since 2009 in CSV files on GitHubįrom this play-by-play data, the following attributes were selected for inclusion in the data set: ![]() NFL play-by-play data from nflscrapR was used for this research. The actual pass play percentages, however, were 61%–62% in 2015–2019. ![]() In 2015–2018, optimal pass play percentages were 76%–83%. The optimum pass percentage in 2019, therefore, should have equaled, but not exceeded, 73%, to provide sufficient unpredictability. Teams’ 2019 passing rates were more than 73% at these WPs. Passing is too predictable at WPs less than 13% because running provides a higher EPA at these WPs. Teams, however, would not want to pass so often to make passing too predictable. Maximizing passing is desirable because passing almost always provides more EPA than running. ![]() At the break-even WP of 13%, pass play percentage was 73%. Teams that are winning run more, and teams that are losing pass more. Pass play percentage decreases as WP increases. Theoretically, at this WP, pass plays became sufficiently unpredictable such that passing was more effective than running. The break-even WP-where pass plays start to have a higher EPA than run plays-was 13% in 2019. Meanwhile, it is predictable for an offense to pass when its WP is low, making pass plays less effective. Presumably, this is mostly because run plays are unexpected in come-from-behind situations. At low WPs, run plays have their highest EPAs and even have higher EPAs than pass plays. The relationship between EPA and Win Probability (WP) is the starting point for determining the optimal pass-run percentage. Nevertheless, Yui’s article suggests that the correct passing equilibrium is far from current run/pass ratios. A recent article by Lau Sze Yui concludes here, however, that passing on every play is not viable. NFL passing plays have higher expected points added (EPA) than running plays in almost every game situation. ![]()
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