Simple Statistics for the Win: Georgia Tech Football Uses Data to Identify Top Recruits

In 2011, the movie Moneyball showed how the Oakland Athletics used the power of statistics to assemble and manage a winning baseball team. The film renewed public interest in the role of data in sports, but in events ranging from the Olympics to auto racing, savvy coaches and managers have relied on analytics for decades.

Both professional and college football teams have used statistics to analyze their competition, predict performance, assess the impact of training, and even gauge how diet and sleep affect players before a big game. Now the coaching staff for the Georgia Tech Yellow Jackets is using statistical analysis to identify and recruit potential running backs for their team, as well as to assess current player performance and pinpoint player attributes that lead to success. To analyze their data, the Georgia Tech football program relies on Minitab Statistical Software.

The Challenge

Lamar Owens, running backs coach for the Georgia Tech Yellow Jackets football team, uses Minitab Statistical Software to analyze data on running back prospects—helping him narrow the field from hundreds of potential players to only the players with attributes fitting the playing style of his team. Photo Credit: Danny Karnik

Football teams gather a great deal of information about potential recruits—often including highlight films, general statistics such as height, weight, and bench press capabilities, as well as athletic ability scores based on their performance in events called combines. Combines are hosted regionally by high schools or colleges to test prospective players on skills such as sprinting, jumping, and lifting—which can give football teams information about players’ quickness, agility, and strength, and allow them to estimate a player’s potential on the field.

Lamar Owens, running backs coach for Georgia Tech, seeks to recruit the best new talent for his team each year, but he found narrowing down hundreds of high school running back prospects was a time-intensive and inefficient effort. Combing through the highlight reels and combine statistics for every prospect was challenging, and factoring in the opinions and evaluations of multiple coaches made objective decisions about potential running backs even more difficult.

But Owens, a trained Six Sigma black belt, has always been interested in continuous improvement—and he saw an opportunity to apply Six Sigma tactics to analyze and improve his recruiting processes. “I became interested in Six Sigma because I love how being data-driven could apply to the way we operate in football,” says Owens. “Narrowing the scope from hundreds of potential prospects to a handful could be made easier if we could analyze all of our player and prospect data and be able to identify the attributes that make for the best running backs for our team.”

With no shortage of data in hand, Owens turned to Minitab Statistical Software to help him analyze his data and draw conclusions that would streamline his team’s recruiting process.

How Minitab Helped

To establish a screening process for high school prospects, Owens analyzed performance data for running backs who attended the latest NFL scouting combine. He used data from top-tier college football players in attendance to determine an upper limit for evaluating high school prospects.

He created summary reports to visualize the distribution of data and to gather descriptive statistics, such as mean, median, and standard deviation, to represent the top-tier running back performance in each of the NFL combine’s tests. The combine assessments included the 40-yard dash; a three-cone drill that demonstrates a player’s ability to change directions at high speeds; a shuttle run that tests lateral quickness as well as short, sudden bursts of energy; vertical and broad jumps; and bench press.

Georgia Tech used Minitab’s graphical summary reports to visualize important summary statistics that capture the performance of NFL running back prospects on tests such as the shuttle run, shown on the report above.

Owens also gathered data from Georgia Tech running backs who performed the same series of tests, then ran the same graphical analysis in Minitab. He used this data to establish a lower limit for evaluating high school prospects.

Owens also ran graphical summaries on the performance data from his own running backs. For each event, he used the median scores of his players as the lower limit that new prospects must achieve in order to be considered for the team. For example, based on this data, prospects needed to complete the shuttle run at least as fast as 4.32 seconds, the median time achieved by Georgia Tech running backs.

With the NFL prospects’ median time established as the upper boundary and Georgia Tech running backs’ median times established as the lower boundary for each event, Owens could assess prospective running backs from high school teams against a data-based standard. Dozens of prospects were invited to campus to be tested on the same combine events, and then Owens used Minitab to analyze the data he collected on each player.

“Using Minitab for data analysis helped me to see the big picture of the quality of running back prospects,” he says. “If I find a high school sophomore who has numbers similar to high school seniors we’re scouting, or even having numbers close to our current running backs, I know he’s worth paying attention to. Having the data makes it easier to put the spotlight on him earlier in our process.”

You may be wondering if it’s fair for high school prospects to be judged against combine times achieved by collegiate players, including upperclassmen who are NFL prospects. Owens explains that his measurement system is useful in narrowing the field.

“It’s not necessarily about being fair to the high school prospects,” he says. “What we’re looking for is the exceptional high school players—the outliers, and we’ve defined upper and lower spec limits to help us find those exceptional players.

“If there’s a high schooler who comes even close to the lower spec limit, then we think he’s worthy of following up. Our goal with recruiting is to find the best of the best.”

Establishing target ranges for each of the 14 combine events made the initial screening of hundreds of prospective running backs each year much easier. Prospective players who performed within the target range for a given event received 3 points for that event, those below the lower bound of the target received 1 point, and those who exceeded the upper bound of the target received 9 points. Prospects who scored a total of 42 points or above were then evaluated further based on their highlight films.

The scoring model, shown above, is used by Owens to rate and funnel prospective running backs for further evaluation.

Results

Using the new rating system made it much easier for Owens and his staff to rank hundreds of running back prospects, and to identify which players showed the most potential and should be evaluated further.

“Before we implemented this new system and started rating prospects more systematically, there was too much subjectivity in ranking the player’s athletic ability,” says Owens. “That made it tough for us to know which prospects we should focus on. The Minitab analysis helped us to get the new rating system in place, and now I’m able to make much more objective decisions based on the data we’re collecting on prospects and the yearly NFL combine event data, as well as performance data from our team and individual players over time.

“The new system really cut down on the amount of time I spent digging up data on prospects, as well as in ranking them for further evaluation. What I learned here also saved us time and eased our process for evaluating the players on film.”

Owens also notes there is a greater understanding, among both the coaching staff and players, about what conclusions can be drawn from all of this data. “I’m often sharing ideas with people who don’t really care about the data as it appears in spreadsheets, but showing graphs really connects the dots for them and allows for meaningful conclusions to be made,” he says.

“But given the demands of being a coach, I don’t always have a lot of time to analyze data and put together the right visuals to tell the story of the data. Minitab is great because it allows me to put the data in, run a few tests, and then the graphs automatically populate.”

The new system has also highlighted attributes of running back performance that matter for their team, making it easier for them to identify those attributes in prospects and to discount attributes that don’t correlate to potential on the field. “For our team, we’ve found that height and weight don’t matter so much for our running backs,” says Owens. “What matters more is their performance on the shuttle run and three-cone drill, because these events tell us about their lateral quickness and ability to change direction.”

While this new rating system has only been implemented in their scouting process for running backs over the last couple of years, Owens is hopeful the lessons he’s learned will help streamline the scouting of other positions on the Georgia Tech football team in the future.

“I know how helpful this new process has been for me in recruiting our running backs, but we’re continuing to tweak the new system as we go and each year I find that it’s getting a little bit better,” Owens says. “Introducing Minitab has been huge for us, though—the bottom line is that Minitab makes it simple to quickly analyze our data and get answers.”

ORGANIZATION

Georgia Tech Yellow Jackets

OVERVIEW

  • Founded in 1892
  • Four Division I-A college football national championships and fifteen conference titles
  • Over 150 alumni who have played in the NFL
  • Also known as the “Ramblin’ Wreck”

CHALLENGE

Improve the process for recruiting running backs

PRODUCTS USED

Minitab® Statistical Software

RESULTS

  • Implemented a new rating system for ranking running back prospects
  • Saved time by making it easier for the coaching staff to identify the strongest prospects
  • Confirmed the reliability of player film evaluations
  • Revealed which player attributes matter most and which are less critical
  • Increased the value of data on prospects, current players, and overall team

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