Cleveland Browns: 3 keys to understanding the use of analytics

Nov 21, 2015; Stillwater, OK, USA; Oklahoma State Cowboys defensive end Emmanuel Ogbah (38) causes a fumble by Baylor Bears quarterback Jarrett Stidham (3) in the second quarter at Boone Pickens Stadium. Ogbah recovered the fumble. Mandatory Credit: Tim Heitman-USA TODAY Sports
Nov 21, 2015; Stillwater, OK, USA; Oklahoma State Cowboys defensive end Emmanuel Ogbah (38) causes a fumble by Baylor Bears quarterback Jarrett Stidham (3) in the second quarter at Boone Pickens Stadium. Ogbah recovered the fumble. Mandatory Credit: Tim Heitman-USA TODAY Sports /
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Aug 13, 2015; Cleveland, OH, USA; Cleveland Browns linebacker Nate Orchard (48) gets a hand on Washington Redskins quarterback Kirk Cousins (8) as Washington Redskins tackle Willie Smith (60) blocks during the second quarter in a preseason NFL football game at FirstEnergy Stadium. Mandatory Credit: Ken Blaze-USA TODAY Sports
Aug 13, 2015; Cleveland, OH, USA; Cleveland Browns linebacker Nate Orchard (48) gets a hand on Washington Redskins quarterback Kirk Cousins (8) as Washington Redskins tackle Willie Smith (60) blocks during the second quarter in a preseason NFL football game at FirstEnergy Stadium. Mandatory Credit: Ken Blaze-USA TODAY Sports /

Instead, the Browns player personnel department has developed an ideal of what a successful NFL player looks like at each position in the scheme. In order to create this “ideal” player, analytics and traditional football analysis are employed in unison. Working together, analytics and traditional football scouting create a plan to build the Browns future player personnel.

The traditional football people put together a scheme. They then work with the scouting department and analytics departments to define the ideal player to fit each role within the scheme.

The analytics department then begins searching for key performance indicators.

A metric is a method for measuring something. In this case, a metric is used to measure if a player can succeed at the NFL level. The “ideal” player for each position is a metric and it is the standard by which players are measured.

A key performance indicator are individual events that can be used to predict future performance. For example, the split-10 of a 40-yard dash is key performance indicator (KPI) of short-yard burst. Another KPI could be a college statistic. For example, Nate Orchard had 18.5 sacks for Utah in 2014. Those sack numbers could be a KPI for future sack numbers in the NFL.

The question for the Browns analytics department is which KPIs translate into future performance. Current NFL Network analyst Charley Casserly raises possibly the most serious objection to the use of analytics in the NFL. He questions the use of statistics as KPIs. His question is whether statistics are accurate representations of on field performance.

For example, a player may sack the quarterback. Great! But was the sack the result of the player’s ability to defeat the blocker, close to the quarterback and finish? Or was it a result of a defensive scheme that gave the player a free run to the quarterback. In the first case, the sack is a valid KPI. In the second place, the sack is not a KPI. How can you tell whether the sack was ability or scheme?

Answer: you cannot. As only coaches have access to the defensive scheme, they are the only ones who know exactly what happens on any particular play. This is especially true for offensive lineman. What is credited to the offensive line as a sack may really have been a missed assignment on the part of a running back.

Analytics departments will always be playing a guessing game when it comes to KPI. But the NFL draft is a guessing game anyway. The use of analytics is to predict future performance and mitigate against loss. No approach is perfect. But an analytical approach, with all its uncertainties, may still be more successful than traditional scouting methods.

The Browns will certainly show us if it can be successful or not. Now that all of this has been laid out, let us look at how the Browns up this all together in the 2016 NFL Draft.

Next: A case study on analytics