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Interview: Brian Hampton, San Francisco 49ers on Playing Football the Analytics Way

We discuss the role of analytics in football, the underrated challenges, evolution since the era of draft trade value chart and analytics-supported team selection.

Brian Hampton 49ersBrian Hampton is in his 11th season with the 49ers and his 5th as Director of Football Administration & Analytics. Previously Hampton served as the team's Manager of Football Operations for two seasons and as an analyst before that. He originally joined the 49ers as a football operations intern in late 2003.

In his current role, Hampton is responsible for contract negotiations, strategic planning & management of the club's player compensation budget and salary cap. Hampton also manages the football R&D efforts, providing frequent analysis on special projects for the owner, head coach, general manager & president.

Here is my interview with him:

Anmol Rajpurohit Q1. In today's world, how much does Analytics influence the game of Football? What are the typical decisions for which the coaches prefer to rely on Analytics?

NFL LogoBrian Hampton: Analytics is not new to football but it has been developing quickly over the last decade. Each NFL team has multiple Quality Control Coaches whose primary function is a mixture of breaking down plays and applying predictive analysis to determine likely sequences and cues that can be built into the game plan. Most of these coaches wouldn’t consider themselves to be using analytics but really that is just because the approach has been used for so long that it just seems like part of coaching to them.

There is an anti-technology rule in place in the NFL which prevents anyone on the sideline or in the coaching booth from using a computer or anything of the sort. Anything that is going to be calculated has to be done in advance and memorized or brought on paper. Hence the application of analytics being primarily in the time leading up to the game and not during the game itself. Every coaching staff has a card, often laminated, with charts and guides to aid in certain decisions. For competitive reasons I won’t divulge any of the specific contents of the cards but in general they contain strategy decisions for a variety of game situations based on careful calculations using many years of game data. If a decision that might be faced during a game is a complicated one and the coach will only have a few seconds to choose a course of action, the grunt work in making the decision has to be done well in advance.

AR: Q2. What are some of the most underrated challenges of Football Analytics?

BH: If you rank the major team sports by the amount they use analytics you will find the order to be exactly the opposite of the sports when ranked by the number of people involved in a typical play. Baseball is 1 on 1 and is the most advanced. To be fair there are some amazing advances in individual sports as well. Basketball has 1 on 1 aspects and never more than 5 on 5 and ranks 2nd. Hockey is similar to basketball with the addition of a goalie and the added wrinkle of teams playing with a man advantage. Soccer and Football are both 11 on 11 but if soccer “plays” are broken down into passing sequences then only a handful of people are in a specific play. Football truly has an important role for all 22 players on each play with the success of each person highly dependent on the execution of assignments by others.

football analytics
The biggest challenge in football analytics is getting a sufficient amount of reliable data. To do this properly requires evaluating each player on each play when you at best know the assignment of half of the players on the field and many of them have adjustments and options impacting their tactics. This means our data is bound to have a lot of “noise” in it. We have to account for much more just to get to the starting point of analysis.

AR: Q3. What are your thoughts on the evolution of Football Analytics since the era of draft trade value chart? Today, how important is the role of Analytics in the team selection strategy?

NFL_DraftBH: The original draft trade value chart was built from observations, not value. The result of negotiated trades that had been executed over many years were charted under the assumption that the value of the picks were equal in all historical trades. A line of best fit was translated into numerical values and those values could be compared to evaluate future offers. The teams using that original chart just wanted to make sure they were getting fair value or better based on historical trade values which in turn made them more confident in doing those deals, they traded more often and the chart proved itself accurate as more and more deals came in along the same lines.

When Bill Walsh used the chart but instinctively felt like he was getting better value by trading down than trading up, he questioned the methodology and commissioned an analytical project to create a new chart from scratch. The 49ers chart was born. A variety of factors (such as the rookie wage scale) change the relative values of picks so the chart we use continues to evolve. This is one area where an analytical approach has gained the most acceptance because it is almost entirely about math and not about technical football details. This opened a door to apply similar concepts to other areas of putting a team together.

Now many teams use a form of similarity scores to compare prospects to pros and the data being collected is rapidly expanding. Scouting is and will remain to be the most critical part of the process at both the beginning (identifying the pool of players) and end (marrying all of the quantitative and qualitative information about the player and his fit to the team) but analytical components are now contributing very heavily in between to inform the decisions.

The second and last part of the interview is here.