Now we tally double-teams regardless of the position combination, so running backs and tight ends count too. Additionally, we were previously tallying only double-teams between two offensive linemen. Improved handling of double-teams: We corrected a number of false positive rush-wins when a rusher beats one lineman in the double-team but not the other. Although we were thrilled with the initial results from our metrics, we've made a number of significant improvements for 2019: This was one of the first major projects using player tracking in football, and much of our efforts are quite experimental. Our pass-blocking and pass-rushing metrics have been in use for more than a full season now, and we've learned a tremendous amount since we rolled out the initial version. We can marry our metrics to other advanced metrics such as expected points and win probability to directly measure the effect individual performance has on team success. We can now objectively assess individual player performance quickly and accurately, at a scale and scope not possible before. These stats offer a novel way of understanding what drives the success or failure of pass offenses and defenses. When you see Pass Block Win Rate (PBWR) or Pass Rush Win Rate (PRWR) powered by Next Gen Stats, these metrics are what we're referring to. You might find these metrics appear on various ESPN shows and in our articles. If a passer throws at 1.8 seconds after the snap, does that mean he only had 1.8 seconds to throw, or did he execute his read quickly? Our metrics know the difference. Also, time in pocket metrics don't know the difference between a quick read and release by the quarterback and ineffective pass protection. Our win rate metric isolates line play from those other factors. A QB pressure can occur for several reasons other than unreliable pass protection, such as good coverage, poor route-running or missed reads by the quarterback. Metrics like QB pressures and time in pocket might be useful, but they can be misleading. And just as importantly, we know how long after the snap it occurred. When a pass-rusher beats his block, we can tell which blocker allowed the pressure. Our model uses the location, proximity and orientation of each player relative to every other player throughout a play to determine who is blocking whom. It's actually pretty simple - there's no fancy machine learning involved. The end result is that we can assess individual performance and team-level performance in the trenches separate and apart from the performance of the quarterback, receivers and secondary. We can also know who is blocking whom on every snap, who was double-teamed, who got pressure from the edges and who got pressure by collapsing the pocket. Now we finally have objective individual stats for linemen for their most critical tasks - defending and attacking the passer. Our model of pass blocking harnesses player tracking data from NFL Next Gen Stats. Likewise, our Pass Rush Win Rate metric tells us how often a pass-rusher is able to beat his block within 2.5 seconds. Our new Pass Block Win Rate metric tells us the rate at which linemen can sustain their blocks for 2.5 seconds or longer. We created better pass-rusher and pass-blocker stats: How they workĮSPN Analytics is pleased to present a revolutionary new way of measuring the pass-block and pass-rush performance of individual NFL players. Read the abbreviated explanation on how they work, or skip to the full details on why they matter and what they can tell us. You have reached a degraded version of because you're using an unsupported version of Internet Explorer.įor a complete experience, please upgrade or use a supported browser
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