Can Player Intelligence Be Used to Find Those Who Will Score Points?

One of the most important numbers in a college football game is the number of points scored.  If your team scores more points than your opponent during a game, then you win.  If your team scores fewer points than your opponent, then you lose.  It’s that simple!  What is difficult to determine is how your offensive system will score points against your opponent’s defensive system and vice versa.  Special teams are also important systems in this equation for putting points on the board.  Although offensive, defensive, and special teams play critical roles in determining how points are scored, it is the players operating within a particular system who actually execute the plays and score the points in the game.

 

On offense, these players can be Running Backs or Fullbacks rushing for touchdowns, Wide Receivers or Tight Ends making receptions for touchdowns, and Quarterbacks throwing the ball or rushing for touchdowns.  On defense, any player on the field can potentially score points on an interception, fumble recovery, or safety.  On special teams, a Kicker or Kick Returner can score.  At Adaptive Datamatics, we have been interested in identifying some of the individual characteristics that are related to players scoring points during a game.  We believe that understanding the personality, motivation, and cognitive skills of a player can provide exciting breakthroughs in player recruiting, assessment, and selection at both the collegiate and professional levels.  This article is a short summary of some ground-breaking findings related to player characteristics and points scored during a game.

The Study

The participants in this study included 144 male, football players competing at the collegiate level.  Before the beginning of the season, we got permission from the head football coach and athletic director to interact with the players and collect player data.  We measured a variety of physical and mental player characteristics.  The physical characteristics included height, weight, wing span, hand span, physical strength, speed, agility, and balance.  We measured mental characteristics using a short 10-minute online test.  The mental characteristics included reaction time to visual-spatial information, player motivation, and intelligence.  Here we were focused on player visual-spatial intelligence.

 

Player intelligence was measured by the person’s ability to reason with abstract, non-verbal information and then make appropriate decisions based upon the player’s ability to recognize a variety of non-verbal patterns.  This was defined as a player’s visual-spatial intelligence, which was measured by our proprietary web-based test.   Higher scores on the test indicated higher visual-spatial intelligence, and lower scores on the test indicated lower visual-spatial intelligence.  We hypothesized before the season started that players with higher visual-spatial intelligence would be those who would have a greater likelihood of scoring than players with lower visual-spatial intelligence.

 

At the end of the regular season for 2013, we collected scoring data from the athletic association in which the college team played.  Specifically, we recorded individual scoring statistics for every player on the team.  If football players in our sample scored any points during the 2013 season in any position on offense, defense or special teams, then they were designated as Scorers.  If players did not score any points, then these players were designated as Non-Scorers.  Scorers and Non-Scorers in this sample of players were compared to each other on a variety of different physical and mental characteristics.

Key Findings

We found that

  • Scorers were significantly faster than Non-Scorers as measured by their 40 yard dash times. In addition
  • Scorers were significantly more agile than Non-Scorers based upon their 5-10-5 short shuttle times.

The Scorers and Non-Scorers did not differ significantly in average height.  In terms of mental player characteristics, we did find support for our hypothesis.

We discovered that Scorers had significantly higher visual-spatial intelligence than Non-Scorers as measured by our intelligence test.  Scorers were better able to

  • reason with non-verbal information
  • recognize visual patterns
  • make more accurate decisions as compared to Non-Scorers.

This ability to use non-verbal information was highly related to a player’s ability to score points during the 2013 college football season.  According to the athletic association’s website, the players who had high visual-spatial intelligence scored points in several offensive, defensive, and special teams positions; these included

  • Quarterback, Running Back, and Fullback with rushing touchdowns
  • Wide Receiver with touchdown receptions
  • Kicker with extra points and field goals
  • Defensive Back and Line Backer with touchdowns from interceptions
  • Kick Returner with touchdowns.

After controlling for physical player characteristics, the relationship between visual-spatial intelligence and scoring points remained statistically significant!

See the figure below for a graphical illustration of the relationship between average visual-spatial intelligence between Scorers and Non-scorers by offensive position.  Scorers are represented by green and Non-scorers are represented by blue.  FB=Fullback, QB=Quarterback, RB=Running back, and WR=Wide Receiver.

Looking Ahead

What can we do with this information?  Using internet-based web assessments, we believe that it is possible to identify players during the recruiting process who can score points based on their mental characteristics.  We feel this is especially the case with visual-spatial intelligence.  At Adaptive Datamatics, we think that we are closer to identifying those hard-to-measure characteristics that lead to winning championships.  We are the first to provide scientific evidence in measuring true “Football Smarts” in college football.

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