ESPN Creates Better Pass-Rusher and Pass-Blocker Stats
Oct 6, 2018 20:49:53 GMT -6
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Post by Purple Pain on Oct 6, 2018 20:49:53 GMT -6
We created better pass-rusher and pass-blocker stats: How they work
More at the link: www.espn.com/nfl/story/_/id/24892208/creating-better-nfl-pass-blocking-pass-rushing-stats-analytics-explainer-faq-how-work
ESPN 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.
OK, what is this exactly?
Our new Pass Block Win Rate metric tells us the rate at which linemen can sustain their blocks for 2.5 seconds or longer. 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 model of pass blocking harnesses player tracking data from NFL Next Gen Stats.
So, what can these stats help show?
Now we finally have objective individual stats for linemen for their most critical tasks -- defending and attacking the passer. 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. 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.
How do they work?
It's actually pretty simple -- there's no fancy machine learning involved. 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. When a pass-rusher beats his block, we can tell which blocker allowed the pressure. And just as importantly, we know how long after the snap it occurred.
Why are they better than stats?
Metrics like QB pressures and time in pocket might be useful, but they can be misleading. 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. Our win rate metric isolates line play from those other factors. Also, time in pocket metrics don't know the difference between a quick read and release by the quarterback and ineffective pass protection. 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.
Where could these stats lead?
You might find these metrics appear on various ESPN shows and in our articles. 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. These stats offer a novel way of understanding what drives the success or failure of pass offenses and defenses. We can now objectively assess individual player performance quickly and accurately, at a scale and scope not possible before. We can marry our metrics to other advanced metrics like Expected Points and Win Probability to directly measure the effect individual performance has on team success.
OK, what is this exactly?
Our new Pass Block Win Rate metric tells us the rate at which linemen can sustain their blocks for 2.5 seconds or longer. 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 model of pass blocking harnesses player tracking data from NFL Next Gen Stats.
So, what can these stats help show?
Now we finally have objective individual stats for linemen for their most critical tasks -- defending and attacking the passer. 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. 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.
How do they work?
It's actually pretty simple -- there's no fancy machine learning involved. 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. When a pass-rusher beats his block, we can tell which blocker allowed the pressure. And just as importantly, we know how long after the snap it occurred.
Why are they better than stats?
Metrics like QB pressures and time in pocket might be useful, but they can be misleading. 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. Our win rate metric isolates line play from those other factors. Also, time in pocket metrics don't know the difference between a quick read and release by the quarterback and ineffective pass protection. 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.
Where could these stats lead?
You might find these metrics appear on various ESPN shows and in our articles. 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. These stats offer a novel way of understanding what drives the success or failure of pass offenses and defenses. We can now objectively assess individual player performance quickly and accurately, at a scale and scope not possible before. We can marry our metrics to other advanced metrics like Expected Points and Win Probability to directly measure the effect individual performance has on team success.