Measuring Motivation

When it comes to recruiting top football players, some of the hardest things to measure are player motivation and leadership potential. Before coaches give scholarships to athletes, they want to know who will give 100 percent effort toward getting better during the off-season, who will play hard from whistle to whistle during practice, and who will go beyond expectations to lead his team to a game winning performance. At MVR, we are interested in quantifying hard-to-measure player characteristics like intelligence, motivation, and leadership, which ultimately lead to winning championships. We have already linked player intelligence to scoring points. Here we are focused on player motivation, leadership potential, and athletic performance. Two studies are discussed.

In Study 1, we show how player motivation is measured using our web-based assessment system. Then we describe how it is related to coordination, balance, and core strength. A subfactor of our motivation survey is leadership potential. In Study 2, we describe how leadership potential is related to coaches’ ratings of recruit athletic performance.

Study 1

The players in this study included approximately 150 college athletes who played football during the 2013 season. We received permission from the college coaches and athletic directors to interact with the athletes during the season. Motivation was measured using a 7-item self-report survey given to the players at the beginning of the season. Using our online assessment, each player rated himself on a True-False scale about how he would act in a variety of different situations. This survey took less than 5 minutes to administer online. Based on their responses, each player was placed in one of three motivation categories.

  • High Motivation
  • Average Motivation
  • Low Motivation


After placing each athlete into one of three motivational categories, we measured the player’s balance using the Balance Error Scoring System (BESS) Test, which is a reliable measure of coordination and balance. We relied on the results of the single leg balance—basically the person stands on their nondominant foot on a firm surface for 20 seconds, with hands on hips and eyes closed. Errors like opening the eyes or moving out of the proper stance lower a person’s score. For this proof of concept, we hypothesized that athletes who are highly motivated based on their survey results would perform better than all other athletes. In other words, highly motivated players would have a lower number of errors on the BESS test than others with average or low motivation. Here is what we found.

As shown in the figure above, players who were classified as highly motivated had on average 4.7 errors on the BESS test. In contrast, players who were classified as average in motivation had slightly more errors in the balance and coordination test with 5.25. As expected, the players who were classified as having low motivation produced the greatest number of balance and coordination errors, with an average error rate slightly above 6. This was the pattern of results we expected to find. In other words, highly motivated players had better balance with approximately 20% fewer errors than players with low motivation. These results did not change when height and weight were factored into the analysis.


For the next test, we measured the athlete’s core strength by counting the total number of back squats the athlete could perform without stopping. We used the normalized number of repetitions the athlete could do. For example, if a running back weighed 180 pounds and did 15 repetitions of 200 pounds, we calculated the total weight lifted (e.g., 15 reps x 200 pounds equals 3,000 total pounds). Then, we divided the total weight lifted by the athlete’s body weight to get the normalized number of back squat repetitions (i.e., 3000 lifted pounds divided by 180 pounds body weight equals 16.67 normalized reps). This allowed us to place every player on the same scale in terms of number of repetitions while controlling for the player’s weight. We expected highly motivated players to be significantly stronger than everyone else.

The figure above shows that highly motivated players were significantly stronger than players with lower motivation. Specifically, players with low motivation did a little over 11 weight-normed repetitions. Athletes with average motivation were slightly higher at 11.6 weight-normed back squat repetitions. As expected, the highly motivated athletes were significantly stronger than everyone else, completing over 14 weight-normed back squats. In other words, they completed approximately 20% more back squat repetitions than the other less motivated players! This illustrates how the general motivation of a player might impact his physical performance!

This study shows that with our web-based survey of player motivation, we are able to identify highly motivated athletes who are stronger and better coordinated than less motivated players who are weaker and less coordinated. Our work is the first of its kind to link self-report measures of motivation to physical strength and coordination. MVR’s survey of player motivation can be used in a recruiting context to determine who will be stronger and better coordinated based on a web-based testing session.

Now that we’ve linked player motivation to physical performance, this next study links coaches’ recruit ratings of player performance to individual player’s self-rating of leadership motivation. Using our web-based survey, we expect that players who are categorized as high potential leaders should stand out to coaches when evaluated for a football scholarship. As a result, these players should be identified more often as top recruits by college football coaches as compared to players who do not have high leadership potential.

Study 2

This study involved over 300 nationally recruited high school football players in the class of 2013. These athletes were recruited by a university football program. Before the recruits were invited to come to the university on an official visit, six members of the football coaching staff evaluated players on a variety of different factors. These factors included height, weight, speed, position skills, character, and on-the-field performance in highlight videos. Each athlete was given an overall grade ranging from A to F, with an A being a high performing recruit and F a low performing recruit. The number of high school recruits invited for an official visit was approximately 105.

While the recruits were on campus, they were asked to complete our web-based assessment of leadership potential. This assessment is a sub-scale of our motivational survey described in Study 1. Based on the survey results, players were identified as having high leadership potential or not having leadership potential. We hypothesized that players who were classified as having high leadership potential according to our web-based assessment would also have a high likelihood of receiving the highest recruiting rating by coaches. The results can be seen in the figure below.

The figure above supports our thinking about leadership potential and coaches’ ratings of recruits. Of all of the recruits who received a rating of A by the college football coaches, over 65% were identified by our web-based assessment as having high leadership potential. This is consistent with the belief that most players who are top-rated athletes are also good leaders on and off the field. In contrast, a large percentage of recruits who received lower coach ratings were also identified by our system as having low leadership potential. It should be noted, however, that some of the lower-rated recruits were identified as having leadership potential. A very reasonable explanation for this result is that some lower-rated recruits may not be as talented as other players, but they do have the ability to lead and motivate team members on and off the field in different situations. Our assessments measure this general tendency. Some of those players may be known as your “Locker Room Guys” who may not show a lot of overall talent but are worthy of being recruited because of their ability to motivate others and lead. This is one of those previously intangible factors that we have been able to measure and successfully use to evaluate prospects.

In summary, the results across these two studies show that motivation and leadership potential can be measured and used to identify athletes with the ability to play at the next level. Our web-based system is able to identify those players with high motivation and high leadership potential. This information has been successfully used by college coaches to build championship winning teams.

MVR Recruiting Staff

Player Intelligence

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 MVR, we have been interested in identifying some of the individual characteristics that are related to players scoring points 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, nonverbal information and then make appropriate decisions based upon the player’s ability to recognize a variety of nonverbal 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.
  • 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. Compared to Non-Scorers the Scorers were better able to:

  • reason with nonverbal information
  • recognize visual patterns
  • make accurate decisions

This ability to use nonverbal 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, including:

  • Quarterback, running back, and fullback with rushing touchdowns
  • Wide receiver with touchdown receptions
  • Kicker with extra points and field goals
  • Defensive back and linebacker 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 MVR 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 MVR, 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.

MVR Recruiting Staff

Why the Wonderlic Fails to Predict Football Success

The Wonderlic Personnel Test is a well-established measure of intelligence that has been shown to predict performance in a variety of traditional jobs. It tests a person’s understanding of, among other things, analogies, arithmetic, directions, disarranged sentences, logic, geometric figures, and word definitions. Primarily it is a verbal-linguistic measure involving words, letters, numbers, and symbols. One of the best known users of the Wonderlic is the National Football League (NFL). In the 1970s the NFL started to use the Wonderlic as an official part of its player evaluation system. During the league’s scouting combine potential rookies are given the 50-item, multiple-choice exam. The score is the number of correct answers a player can get in 12 minutes. An average score for all people (and about the average for all football players) is 20.

A player’s score is supposed to give coaches and player evaluators an important insight into a player’s potential value on the football field.

But does it?

Measuring the Wrong Type of Intelligence

The consensus answer that has emerged in recent years is an emphatic “no”—the Wonderlic is not a valid predictor of player performance for any skill-level position using a variety of outcomes. Why this is the case shouldn’t really be that surprising if we consider what type of intelligence the Wondelic is actually measuring. It is mainly a test of verbal-linguistic information. Neurologists and other scientists who have studied learning and the brain have found that such information is processed using focused attention in a serial manner, functions that happen primarily in the brain’s left hemisphere. Crystallized intelligence is one of the common terms for what the Wonderlic is measuring. Scientists now recognize, however, a number of additional kinds of intelligences, from kinesthetic to emotional. And a key one for the purpose of measuring success in certain sports such as football is visual-spatial intelligence. Unlike verbal-linguistic information, visual-spatial information is processed using both focused (serial) and divided (parallel) attention, functions that depend primarily on the brain’s right hemisphere. Fluid intelligence is one of the common terms for what visual-spatial tests measure.

Testing Quarterbacks

If you were drafting a team of crossword puzzle players it might make sense to test them on the Wonderlic, but a number of studies have shown it is a meaningless measure for football players. Why look for high scores in potential quarterbacks, when their average (24) is lower than both centers (25) and offensive tackles (26)? If you passed on Donovan McNabb because of his below average score of 14 in the first round of the 1999 draft, in favor of any of the four other quarterbacks with higher scores taken in that round, you lost out on the player with the best career.

Our research here at MVR shows that quarterbacks, on average, have the highest abstract reasoning test scores as compared to all other positions while also having the fastest reaction time to visual and spatial information. New smart tests that show how quickly and accurately players can make judgments based upon visual or spatial information are promising alternatives to the Wonderlic. They can be readily administered by computer and instantly adapt to a player’s answers, based on their previous response patterns and estimated abilities. The result is a specific measure of important aspects that contribute to player performance, such as information processing load, reaction time, decision accuracy, and attention.

The Bottom Line

The NFL would be wise to pick a visual-spatial test in the first round, and let the Wonderlic be an undrafted walk-on.

MVR Recruiting Staff