New Study Identifies NBA Players Who Shoot Too Much

To reach the NBA Finals, Russell Westbrook of the Oklahoma City Thunder needs to pass more, especially to his teammate Kevin Durant.  That would be the message that two researchers would send to Thunder coach, Scott Brooks, if given the chance.  Matt Goldman, a graduate student at the University of California, San Diego, and Justin Rao, a research scientist at Yahoo Labs recently named Westbrook as the biggest “chucker” in the NBA because of statistics showing that he shoots much more often than he should, while Durant is classified as an undershooter, whose team would benefit from him taking more chances.

While their statistical theory builds a case for how to achieve optimal efficiency on the court, they don’t explain why elite players make the in-game decisions that they do.  For that matter, what about the high school ball player or the weekend warrior at the gym; how do they make the decision to pass or shoot?  For that, Markus Raab and Joseph Johnson, both sport scientists, have some insights  from their research.

First, let’s do the numbers.  Goldman and Rao dug into the NBA stats archive to analyze over 400,000 team possessions over the last four seasons, 2006-2010, across the entire league.  In a paper and presentation at the recent MIT Sloan Sports Analytics Conference, they presented a model that compares the difficulty of a shot taken in relation to the time remaining on the 24 second shot clock.  Then they compare this with a concept called “allocative efficiency”, or the benefit of equally distributing the ball to any of the five players on the court and also “dynamic efficiency”, or deciding whether to “use” the possession by taking a shot or “continuing” the possession by making a pass.  As the shot clock winds down, the marginal difficulty of a shot considered will need to rise or they risk getting no shot off before the 24 seconds expires, wasting the possession.

They found that most NBA  players are very efficient in their shot selection.  Surprisingly, several elite players are actually not shooting enough, according to their model.  Here is the list of all NBA players analyzed and their score, where a negative number (at the top of the list) represent overshooters.  Joining Westbrook at the top of the list were well-known names like Lamar Odom and Tracy McGrady.  Even bigger names like LeBron James, Ray Allen, Dirk Nowitzki, Chris Paul and Joe Johnson actually show up at the bottom of the list and may hurt their team with their unselfishness.

So, what goes on in these very well-paid athletic brains?  Are the trigger-happy players selfish, over-confident and in need of attention?  Markus Raab, professor at the German Sport University-Cologne, and Joseph Johnson, professor at Miami University of Ohio,  have spent the last ten years studying the decision-making processes of athletes in several different sports, but especially fast-paced games where quick decisions are critical.

Let’s imagine the Thunder point guard, Westbrook, bringing the ball up the floor.  He crosses the half court line and his decision making process kicks in.  The Raab/Johnson process first recognizes that perception of the situation is required before the player can generate all of the different options in his brain.  Just like a quarterback examining and identifying the defensive alignment as he breaks the huddle, the point guard in basketball has to visually process the scene in front of him.  From there, his brain, based on his vast memory of similar basketball experiences, begins to make a list of options.  These can be spatial options, like move the ball left, ahead or right, or functional options like pass or shoot.  

Through research with basketball and team handball players, the researchers found that the most effective strategy is to “take the first” option that the player conceives as that is most often the “correct” choice when analyzed later by experts.  Much like going with your first answer on a test, the more that you deliberate over other choices, the greater the chances that you’ll pick the wrong one.  

However, each player will have their own library of choices stored in their memory and this magical sorting of best options can be influenced by several unique variables.  

One of these pre-determined factors is a personality preference known as action vs. state orientation.  According to Raab, “An action orientation is attributed to players if they concentrate on a specific goal and take risks, whereas a state orientation is attributed to players if they have non-task-relevant cognitions and reduce risk-taking behavior by considering more situative considerations and future behavioral consequences.”  In other words, someone who has an action mentality is more likely to shoot first and ask questions later, while a state oriented player is going to consider more options with more long-term outlook.

For this and similar experiments, Raab and Johnson showed first-person videos of many different basketball in-game scenarios to players of different skill levels and personality types, then froze the scene and asked them to make a quick decision of what to do next with the ball.  They recorded the decision and the time it took to make the decision.  They found that those players who have more of an action orientation, according to a personality test given prior to the drill, were more likely to shoot first and more quickly.  Clearly, Russell Westbrook must fall in this category.

Raab followed up this study with a similar one that measured the difference between intuition-based decisions and more cognitive, deliberate decisions.  A player who “goes with his gut” was shown to make faster and more successful choices than one that over analyzes.  This may help explain the list of elite players who tend to pass more than shoot.  They have more experience and patience to rely on their intuitive feel for the game.  While Goldman and Rao may ask them to be more action oriented, these players have learned that they are often just one more pass away from a much higher percentage shot.

Certainly, this is the tip of the iceberg regarding the psyche of a player at any level.  There are many more variables, some fact-based (I’ve missed my last 5 shots, so I’m going to pass) while some are more emotional, (I don’t want my teammate to get all the glory.)  For now, Thunder fans can only hope that their point guard learns to share.

See also: Are Bank Shots Best In Basketball? and NBA Teams Win With Ethnic Diversity

Stats Vs. Hunches - The Moneyball Era In Sports

Most baseball general managers live in obscurity most of their careers.  Its their first hire, the manager, that usually gets the red hot spotlight, after every win and loss, second-guessed by reporters with recorders and then later by fans.  The GM puts the players on the field and lets the manager and his coaches take it from there.  Billy Beane , Oakland A's general manager, could have also been an unknown, albeit interesting, name to the baseball audience if it were not for author Michael Lewis' 2003 book, Moneyball .  Moneyball was a runaway hit (even today, 5 years later, it is #19 on Amazon's list of baseball books).  It has morphed into a full-fledged catchphrase philosophy used by everyone from Wall Street (where Beane borrowed the concept) to business consulting.  The general theme is to find undervalued assets (ballplayers) by focusing on statistics that your competition is ignoring.  Of course, you have to believe in your metrics and their predictive value for success (why has everyone else ignored these stats?)  The source of most of Beane's buried treasure of stats was Bill James and his Sabrmetrics.  Like picking undervalued stocks of soon to explode companies, Beane looked for the diamond in the dust (pun intended) and sign the player while no one was looking.  Constrained by his "small-market" team revenues, or maybe by his owners' crowbar-proof wallets, he needed to make the most from every dollar.

The combination of a GM's shrewd player selection and a manager who can develop that talent should reward the owner with the best of both worlds: an inexpensive team that wins.  This salary vs. performance metric is captured perfectly in this "real-time" graphic at .  It connects the updated win-loss record for each MLB team with its payroll to show the "bang for the buck" that the GMs/managers are getting from their players.  Compare the steep negative relationship for the Mets, Yankees, Tigers and Mariners with the amazing results of the Rays, Twins and Beane's own A's.  While the critics of Moneyball tactics would rightly point to the A's lack of a World Series win or even appearance, the "wins to wages" ratio has not only kept Beane in a job but given him part ownership in the A's and now the newly resurrected San Jose Earthquakes of soccer's MLS.  Beane believes the same search for meaningful and undiscovered metrics in soccer can give the Quakes the same arbitrage advantage.  In fact, there are rumours that he will focus full-time on conquering soccer as he knows there are much bigger opportunities worldwide if he can prove his methods within MLS.

In baseball, Beane relied on the uber-stat guru, Bill James, for creative and more relevant statistical slices of the game.  In soccer, he is working with some top clubs including his new favorite, Tottenham-Hotspur, of the English Premier League.  While he respects the history and tradition of the game, he is confident that his search for a competitive advantage will uncover hidden talents.  Analytical tools from companies such as Opta in Europe and Match Analysis in the U.S. have combined video with detailed stat breakdowns of every touch of the ball for every player in each game.  Finding the right pattern and determinant of success has become the key, according to Match Analysis president Mark Brunkhart as quoted earlier this year,
"You don't need statistics to spot the real great players or the really bad ones. The trick is to take the players between those two extremes and identify which are the best ones.  If all you do is buy the players that everyone else wants to buy then you will end up paying top dollar. But if you take Beane's approach - to use a disciplined statistical process to influence the selection of players who will bring the most value - then you are giving yourself the best chance of success. Who would not want to do that?"

Not to feel left out (or safe from scrutiny), the NBA now has its own sport-specific zealots.  The Association for Professional Basketball Research (APBR) devotes its members time and research to finding the same type of meaningful stats that have been ignored by players, coaches and fans.  They, too, have their own Moneyball-bible, "The Wages of Wins " by David Berri, Martin Schmidt, and Stacey Brook.  David Berri's WoW journal/blog regularly posts updates and stories related to the current NBA season and some very intriguing analysis of its players and the value of their contributions.  None other than Malcolm Gladwell, of Tipping Point and Blink fame, provided the review of Wages of Wins for the New Yorker.  One of the main stats used is something called a player's "Win Score" which attempts to measure the complete player, not just points, rebounds and assists.
Win Score (WS) = PTS + REB + STL + ½*BLK + ½*AST – FGA – ½*FTA – TO – ½*PF.   (Points, Rebounds, Steals, Blocked Shots, Assists, Field Goal Attempts, Free Throw Attempts, Turnovers, Personal Fouls)

WS is then adjusted for minutes played with the stat, WS48.  Of course, different player positions will have different responsibilities, so to compare players of different positions the Position Adjusted Win Score per 48 minutes or PAWS48 is calculated as: WS48 – Average WS48 at primary position played.  This allows an apples to apples comparison between players at a position, and a reasonable comparison of players' values across positions.  Berri's latest article looks at the fascination with Michael Beasley and some early comparisons in the Orlando Summer League. 

Will these statistics-based approaches to player evaluation be accepted by the "establishment"?  Judging by the growing number of young, MBA-educated GMs in sports, there is a movement towards more efficient and objective selection criteria.  Just as we saw in previous evidence-based coaching articles , the evidence-based general manager is here to stay.

Teaching Tactics and Techniques In Sports

You have probably seen both types of teams. Team A: players who are evenly spaced, calling out plays, staying in their positions only to watch them dribble the ball out of bounds, lose the pass, or shoot wildly at the goal. Team B: amazing ball control, skillful shooting and superior quickness, speed and agility but each player is a "do-it-yourselfer" since no one can remember a formation, strategy or position responsibility. Team A knows WHAT to do, but can't execute. Team B knows HOW to do it, but struggles with making good team play decisions. This is part of the ongoing balancing act of a coach. At the youth level, teaching technique first has been the tradition, followed by tactical training later and separately. More recently, there has been research on the efficiency of learning in sports and whether there is a third "mixed" option that yields better performance.

Earlier, we took an initial look at Dr. Joan Vickers' Decision Training model as an introduction to this discussion. In addition, Dr. Markus Raab of the Institute for Movement Sciences and Sport, University of Flensburg, Germany, (now of the Institute of Psychology, German Sport University in Cologne), took a look at four major models of teaching sports skills that agree that technical and tactical skills need to be combined for more effective long-term learning.Each of the four models vary in their treatment of learning along two different dimensions; implicit vs. explicit learning and domain-specific vs. domain-general environments. 

Types of Learning

Imagine two groups of boys playing baseball. The first group has gathered at the local ball diamond at the park with their bats, balls and gloves. No coaches, no parents, no umpires; just a group of friends playing an informal "pick-up" game of baseball. They may play by strict baseball rules, or they may improvise and make their own "home" rules, (no called strikes, no stealing, etc.). In the past, they may have had more formal coaching, but today is unstructured.

The second group is what we see much more often today. A team of players, wearing their practice uniforms are driven by their parents to team practice at a specific location and time to be handed off to the team coaches. The coaches have planned a 90 minute session that includes structured infield practice, then fly ball practice, then batting practice and finally some situational scrimmages. Rules are followed and coaching feedback is high. Both groups learn technical and tactical skills during their afternoon of baseball. They differ in the type of learning they experience.

The first group uses "implicit" learning while the second group uses "explicit" learning. Implicit learning is simply the lack of explicit teaching. It is "accidental" or "incidental" learning that soaks in during the course of our play. There is no coach teaching the first group, but they learn by their own trial and error and internalize the many if-then rules of technical and tactical skills. Explicit learning, on the other hand, is directed instruction from an expert who demonstrates proper technique or explains the tactic and the logic behind it.

An interesting test of whether a specific skill or piece of knowledge has been learned with implicit or explicit methods is to ask the athlete to describe or verbalize the details of the skill or sub-skill. If they cannot verbalize how they know what they know, it was most likely learned through implicit learning. However, if they can explain the team's attacking strategy for this game, for example, that most likely came from an explicit learning session with their coach.

Types of Domains

The other dimension that coaches could use in choosing the best teaching method is along the domain continuum. Some teaching methods work best to teach a skill that is specific to that sport's domain and the level of transferability to another sport is low. These methods are known as domain-specific. For more general skills that can be useful in several related sports, a method can be used known as domain-general.

Why would any coach choose a method that is not specific to their sport? There has been evidence that teaching at a more abstract level, using both implicit and explicit "play" can enhance future, more specific coaching. Also, remember our discussion about kids playing multiple sports.Based on these two dimensions, Dr. Raab looked at and summarized these four teaching models:
  • Teaching Games for Understanding (TGFU)
  • Decision Training (DT)
  • Ball School (Ball)
  • Situation Model of Anticipated Response consequences of Tactical training (SMART)

The TGFU approach, (best described by Bunker, D.; Thorpe, R. (1982) A model for the teaching of games in the secondary school, Bulletin of Physical Education, 10, 9–16), is known for involving the athlete early in the "cognition" part of the game and combining it with the technical aspect of the game. Rather than learn "how-to" skills in a vacuum, TGFU argues that an athlete can tie the technical skill with the appropriate time and place to use it and in the context of a real game or a portion of the game.

This method falls into the explicit category of learning, as the purpose of the exercise is explained. However, the exercises themselves stress a more domain-general approach of more generic skills that can be transferred between related sports such as "invasion games" (soccer, football, rugby), "net games" (tennis, volleyball), "striking/fielding games" (baseball, cricket) and "target games" (golf, target shooting). 

Decision Training

The DT method, (best described by Vickers, J. N., Livingston, L. F., Umeris-Bohnert, S. & Holden, D. (1999) Decision training: the effects of complex instruction, variable practice and reduced delayed feedback on the acquisition and transfer of a motor skill, Journal of Sports Sciences, 17, 357–367), uses an explicit learning style but with a domain-specific approach. Please see my earlier post on Decision Training for details of the approach. 

Ball School

The Ball School approach, (best described by Kroger, C. & Roth, K. (1999) Ballschule: ein ABC fur Spielanfanger [Ball school: an ABC for game beginners] (Schorndorf, Hofmann), starts on the other end of both spectrums, in that it teaches generic domain-general skills using implicit learning. It emphasizes that training must be based on ability, playfullness, and skill-based. Matching the games to the group's abilities, while maintaining an unstructured "play" atmosphere will help teach generic skills like "hitting a target" or "avoiding defenders". 


Dr. Raab's own SMART model, (best described in Raab, M. (2003) Decision making in sports: implicit and explicit learning is affected by complexity of situation, International Journal of Sport and Exercise Psychology, 1, 406–433), blends implicit and explicit learning within a domain-specific environment. The idea is that different sports' environmental complexity may demand either an implicit or explicit learning method. Raab had previously shown that skills learned implicitly work best in sport enviroments with low complexity. Skills learned explicitly will work best in highly complex environments. Complexity is measured by the number of variables in the sport. So, a soccer field has many moving parts, each with its own variables. So, the bottom line is to use the learning strategy that fits the sport's inherent difficulty. So, learning how to choose from many different skill and tactical options would work best if matched with the right domain-specific environment.  

Bottom-Line for Coaches

What does all of this mean for the coach? That there are several different models of instruction and that one size does not fit all situations. Coaches need an arsenal of tools to use based on the specific goals of the training session. In reality, most sports demand both implicit and explicit learning, as well as skills that are specific to one domain, and some that can transfer across several sport domains. Flexibility in the approach taken goes back to the evidence based coaching example we gave last time. Keeping an open mind about coaching methods and options will produce better prepared athletes.

(2007). Discussion. Physical Education & Sport Pedagogy, 12(1), 1-22. DOI: 10.1080/17408980601060184

Single Sport Kids - When To Specialize

So, your grade school son or daughter is a good athlete, playing multiple sports and having fun at all of them. Then, you hear the usual warning, either from coaches or other parents; "If you want your daughter to go anywhere in this sport, then its time to let the other sports go and commit her full-time to this one." The logic sounds reasonable. The more time spent on one sport, the better she will be at that sport, right? Well, when we look at the three pillars of our Sports Cognition Framework, motor skill competence, decision making ability, and positive mental state, the question becomes whether any of these would benefit from playing multiple sports, at least in the early years of an athlete (ages 3-12)? It seems obvious that specific technical motor skills, (i.e. soccer free kicks, baseball bunting, basketball free throws) need plenty of practice and that learning the skill of shooting free throws will not directly make you a better bunter. On the other end, learning how to maintain confidence, increase your focus, and manage your emotions are skills that should easily transfer from one sport to another. That leaves the development of tactical decision making ability as the unknown variable. Will a young athlete learn more about field tactics, positional play and pattern recognition from playing only their chosen sport or from playing multiple related sports?

Researchers at the University of Queensland, Australia learned from previous studies that for national team caliber players there is a correlation between the breadth of sport experiences they had as a child and the level of expertise they now have in a single sport. In fact, these studies show that there is an inverse relation between the amount of multi-sport exposure time and the additional sport-specific training to reach expert status. In plain English, the athletes that played several different (but related) sports as a child, were able to reach national "expert" level status faster than those that focused only one sport in grade school . Bruce Abernethy, Joseph Baker and Jean Cote designed an experiment to observe and measure if there was indeed a transfer of pattern recognition ability between related sports (i.e. team sports based on putting an object in a goal; hockey, soccer, basketball, etc.)

They recruited two group of athletes; nationally recognized experts in each of three sports (netball, basketball and field hockey) who had broad sports experiences as children and experienced but not expert level players in the same sports whose grade school sports exposure was much more limited (single sport athletes). (For those unfamiliar with netball, it is basically basketball with no backboards and few different rules.) The experiment showed each group a video segment of an actual game in each of the sports. When the segment ended the groups were asked to map out the positions and directions of each of the players on the field, first offense and then defense, as best they could remember from the video clip. The non-expert players were the control group, while the expert players were the experimental groups. First, all players were shown a netball clip and asked to respond. Second, all were shown a basketball clip and finally the hockey clip. The expectation of the researchers was that the netball players would score the highest after watching the netball clip (no surprise there), but also that the expert players of the other two sports would score higher than the non-expert players. The reasoning behind their theory was that since the expert players were exposed to many different sports as a child, there might be a significant transfer effect between sports in pattern recognition, and that this extra ability would serve them well in their chosen sport.

The results were as predicted. For each sport's test, the experts in that sport scored the highest, followed by the experts in the other sports, with the non-experts scoring the poorest in each sport. Their conclusion was that there was some generic learning of pattern recognition in team sports that was transferable. The takeaway from this study is that there is benefit to having kids play multiple sports and that this may shorten the time and training needed to excel in a single sport in the future.

So, go ahead and let your kids play as many sports as they want. Resist the temptation to "overtrain" in one sport too soon. Playing several sports certainly will not hurt their future development and will most likely give them time to find their true talents and their favorite sport.
Abernethy, B., Baker, J., Côté, J. (2005). Transfer of pattern recall skills may contribute to the development of sport expertise. Applied Cognitive Psychology, 19(6), 705-718. DOI: 10.1002/acp.1102

Why The Offsides Flag Has Been "Ruud" to Italy

Two Euro 2008 games and two questionable offsides calls against Italy, one on defense, the other on offense, are still being talked about this weekend. First, in the Netherlands opener, van Nistelrooy scores from an obvious offsides position... except for Panucci, who is lying on the ground next to the goal. In fact, UEFA had to defend their referee for a correct interpretation. The call that did not get an explanation was Luca Toni's offsides on a cross from Zambrotta in the Romania match, which disallowed a first half goal. The first call was deemed correct, the second one was a blatant error.

Calling offsides correctly is one of the most difficult officiating duties in sports. In fact, some have argued that it is nearly impossible given the limitations of the human eye and the number of objects that need to be tracked by one assistant referee. Back in 2004, Francisco Belda Maruenda, M.D. of Centro de Salud de Alquerías in Murcia, Spain, took a look at the eye movements necessary along with their associated durations to determine if it was a humanly possible task. Let's look at his logic.

First, some eye physiology definitions are needed:
Saccadic movements - when we shift our eyes' focus from one object to another, we are making a saccadic movement. As an assistant referee (AR) looks from the ball carrier to the last defender to the offensive players, he needs to make several saccadic movements to take in the whole scene.

Vergence movements - there are two types, convergence (changing gaze from objects far away to objects closer to you), and divergence (just the opposite, near to far).

Accomodation - to change the focus of the eye from far to near or near to far, the convexity of the retina lens needs to change.

All of these eye movements, saccadic, vergence and accomodations take time to accomplish. Let's see how Maruenda added these up for an offsides call:

- the AR needs to keep track of at least four objects, the ball, the last two defenders and the offensive receiver of the pass. There may also be more offensive players to track as well.
- to make saccadic movements from the first object to each of the remaining objects will take about 130ms for the first object and then another 10ms per object after that. With four objects to track, that would be a total of about 160ms.
- if some of the players are on the far side of the field and some on the near side, then a vergence movement and an accomodation would be required, taking an additional 360ms for the accomodation and 640ms for the far to near vergence movement.
- of course, the players are constantly moving during the play, so their position is changing rapidly. If the speed of an offensive player is assumed to be 7.14 m/s, then in 100ms, they will have moved 71cm. This movement could be the difference between an onside position and an offside position. See the diagrams below (taken directly from the article)

Top: No offside, players in correct position.

Bottom: 100 ms later (players' velocity 7.14 m/s), offsides

The conclusion then, is that the total time needed for the AR to focus on at least four different objects in sequential order and process their positions cognitively is beyond the 100ms that would be needed for an offensive player to move from an onside position when the ball is played to a perceived offsides position when the AR finally focuses on him.

There have been some responses to Maruenda's logic, mainly centered on the fact that ARs have long known they can't watch the ball and the last defender, so they instead listen for the sound of the ball being struck while staying focused on the line of defense. This method may be used, but the sound of the crowd, the muted sound of the boot on the ball and the slower speed of sound may also have an effect on this judgement.

There is technology being developed to make offsides calls with multiple cameras, etc., but FIFA is not in favor of taking the flag away from the AR yet, just as they are against obvious goal line technology to watch for goals. It appears the debates and arguments will live on for the near future.

Belda Maruenda, F. (2004). Can the human eye detect an offside position during a football match?. BMJ, 329(7480), 1470-1472. DOI: 10.1136/bmj.329.7480.1470

The Coach's Curse - Mental Mistakes

"Donadoni rues Italian 'mistakes' against Dutch"

"Mental errors cost Demons in regional quarterfinal"

"Mental mistakes doom Rays in loss to Cardinals"


Every day, there is always a new variety of stories linked to the phrase, "mental mistakes".  Either the writer recaps a game, calling out the mistakes or a coach or player claims that mistakes were made. It has become sort of a throwaway phrase, "...we made a lot of mental mistakes out there today, that we need to avoid if we want to get to the playoffs..." The million dollar question then is HOW to reduce these mental mistakes. And, to answer that, we need to define WHAT is a mental mistake?

In a previous post, I introduced the "Sports Cognition Framework", which is a trio of elements needed for success in sports. These three elements are:

- decision-making ability (knowing what to do)

- motor skill competence (being physically able to do it)

- po
sitive mental state (being motivated and confident to do it)

Most of the time, a mental mistake is thought of as a breakdown of decision-making ability. The center fielder throws to the wrong base, the tight end runs the wrong route, or the defender forgets to mark his man, etc. These scenarios describe poor decisions or even memory lapses during the stress of the game. They are not necessarily the lack of skill to execute a play or the lack of confidence or motivation to want to do the right thing. It is a recognition, in hindsight, that the best option was not chosen. In addition to glaring nega
tive plays, there are also missed opportunities on the field (i.e. taking a contested shot on goal, instead of passing to the open teammate).

So, back to the payoff question: HOW do we reduce mental mistakes and poor decisions? Just as we practice physical skills to improve our ability to throw, catch, shoot, run, etc., we need to practice making decisions using a a training system that directly exposes the athlete to these scenarios. Dr. Joan Vickers, who we met during our discussion of the Quiet Eye, has created a new system which she calls the "Decision-Training Model", and is the focus of the second half of her book, "Perception, Cognition, and Decision Training". As opposed to traditional training methods that separate skill training from tactical decision making training, the Decision-Training model (D-T) forces the athlete to couple her skill learning with the appropriate tactical awareness of when to use it.

So, instead of an "easy-first" breakdown of a skill, and then build it up step by step, D-T begins with a "hard-first" approach putting the "technique within tactics" demanding a higher cognitive effort right up front. The theory behind D-T is that the coach is not on the field with the player during competition, so the player must learn to rely on their own blended combination of skill and game awareness. Research from Vickers and others shows that D-T provides a more lasting retention of knowledge, while more traditional bottom-up training with heavy coach feedback delivers a stronger short-term performance gain, but that success in practice does not often translate later in games. Practice and training need to mirror game situations as often and as completely as the real thing.

There are three major steps to Decision-Training (p. 167):

1. Identify a decision the athlete has to make in a game, using one of the seven cognitive skills (anticipation, attention, focus/concentration, pattern recognition, memory, problem solving and decision making)

2. Create a drill(s) that trains that decision using one of the seven cognitive triggers (object cues, location cues, Quiet Eye, reaction-time cues, memory cues, kinesthetic cues, self-coaching cues)

3. Use one or more of the seven decision tools in the design of the drill (variable practice, random practice, bandwidth feedback, questioning, video feedback, hard-first instruction, external focus of instruction)

This post was just to serve as an introduction to D-T. Dr. Vickers and her team at University of Calgary offer full courses for coaches to learn D-T and apply it in their sport. Combined with the visual cues of the playing environment provided by the Quiet Eye gaze control, D-T seems to offer a better tactical training option for coaches and athletes. Coming up, we will continue the discussion of decision-making in sports with a look at some other current research. Please give me your thoughts on D-T and the whole topic of mental mistakes!

See The Ball, Be The Ball - Vision and Sports

The whistle blows and Shaq goes to the line again after being fouled on purpose for the fourth time. And, again, we watch as he takes that awkward stance, looks at the basket and then clanks one of the back of the rim. We wonder how hard this can be... just aim and shoot! Isn't it that simple? Well, not exactly. In our introduction to this series I mentioned the research of Dr. Joan Vickers and her concept of the "Quiet Eye". In her book, Perception, Cognition and Decision Training, she describes this visual targeting pathway:

"...the visual pathway begins when information is registered on the eye's retina by the focal and ambient systems, then travels to the back of the head along the optic nerve and radiates to the occipital cortex, where visual information is registered as billions of features. These then race in parallel fashion both to the top of the head to the parietal cortex (dorsal) and along the sides of the head to the temporal (ventral) areas. There is an integration of information in the somatosensory cortex as the information goes to the frontal cortex, where the goals and intentions reside and plans are formulated for the specific event that is occurring. The flow of information then goes to the premotor and motor cortex at the top of the head before going down the spinal cord to the effectors." P.26

This same process repeats constantly during any athletic event and it is the most critical determinant of the outcome of the game. Just think about the types of visual work that needs to be done by an athlete (as defined by Dr. Vickers):

1. Targeting Tasks - being able to fixate on a target, fixed or moving, to be able to throw, kick or send an object towards it. (i.e. Shooting or passing a baseball, football, basketball, soccer ball, hockey puck, etc.)

2. Interceptive Timing Tasks - being able to recognize, track and finally control an object as it comes at you (aka "catching")

3. Tactical Decision Making Tasks - being able to take in an environmental scan of the field/court and recognize patterns of all the moving objects (i.e. a quarterback scanning his receivers and choosing the best option for a pass).

All of these scenarios require the athlete to focus or "gaze" on the right points in the environment and ignore the rest of the scene. Dr. Vickers' work has been to observe athletes of different skill levels, expert and non-expert, and define the "best practices" of visual control so that the non-expert athletes can be coached to better performance. Her research lab uses "eye-trackers" (see photo) to monitor the focus and gaze of the athlete's pupils as they perform their skills.

For example, she has found that expert baseball hitters focus on the release point of the ball exclusively, rather than random fixations on the pitcher's arm, head, jersey, etc. She found that expert golf putters focus on a specific point on the cup, then a specific point on the back of the ball and remain fixated on the point on the ball after the ball has left the putter blade.

Novices allow their gaze to wander from the ball to the hole, without a very specific focal point on either the cup or the ball. The term "Quiet Eye" comes from these observations that expert performers have consciously chosen points in their space to focus on rather than allowing their eyes to wander and fixate on multiple points (i.e. a "noisy" eye).

So, why does the Quiet Eye work? When we fixate on key points in our field of vision, how does this help our neuromuscular systems perform better? The subconscious part of our brain may be recognizing a pattern that we have seen and experienced before and directing our movements based on this information. Some have called this "muscle memory", meaning our brain has learned through repetition and practice how to throw a ball to a moving receiver at that distance and speed, and so, when presented with a similar scenario, knows what to do. Think about when you shoot a jump shot and sometimes you get that sensation, as soon as it leaves your hand, that the ball is going in. Your brain may be telling you that, based on past experience, when you've executed the same aim and same muscle movement then the ball has gone in.

This takes us back to the discussion we had in our previous post on baseball fielding regarding theories of perception-action combinations. The Information Processing model claims that we perceive the environment first through our senses, primarily our vision. Then, we access our memory to find the rules, suggestions and knowledge that we have gained from past experiences and these memories guide our action in the moment.

The Ecological Psychology model removes the memory access step and claims that our perception of the environment leads directly to our actions, as there is not enough time to access our lessons. If that is true, then how does the Quiet Eye help us? It seems the Quiet Eye is what we need to connect the current scenario (standing on the free throw line looking at the basket) with our lessons learned from the past (how we made this shot hundreds of times before). Research continues on this question and I'm sure we'll come back to this in future posts.

Next time, I will take a look at Dr. Vickers' "Decision Training Model", which builds on the Quiet Eye theory to train athletes to improve their tactical in-game decision making. We will look at the athletes who are known as having good "vision of the field" and how to raise everyone's game to that level.

So Why Can't Shaq Make Free Throws?

The NBA league average for free throw shooting is about 75%. Shaquille O'Neal's career average is 52.4%. Even worse, Ben Wallace's career average is 41.9%. The average for the NCAA Division 1 teams is 69%. The obvious question is why can't Shaq or Ben or Memphis do any better, but the bigger question is why do most of the best basketball players in the world miss 2 or 3 free throws out of 10? Maybe they just haven't heard about Joan Vickers and the "Quiet Eye".

For me, the best science is applied science. The same goes for sports science. Theories, physics, psychology, etc. are only useful in sports if they can be used to improve in-game performance. That's why I have always been a fan of academic work that leads to useful techniques in the field. Professor Joan Vickers of the University of Calgary has been applying her research into the human visual system and its effects on sports performance for over 25 years. She is the discoverer of the "Quiet Eye" skill that has been shown to significantly improve accuracy in targeting and decision-making skills in many sports. In addition to this "gaze control" technique, she also has developed a 7-step teaching process to improve the in-game decision-making of athletes, based partly on their visual perception skills.

She has a new book out that condenses all of these ideas, called Perception, Cognition and Decision Training. Over the next few days, I will do my best to paraphrase and explain the most useful information and techniques, but of course the best source is this book.
For an opening primer on the Quiet Eye, please take a look at this episode and this online video of PBS' Scientific American with Hawkeye himself, Alan Alda, shooting free throws.

The Sports Cognition Framework

So, why should athletes and coaches be interested in all of this cognitive science stuff? They have been playing and coaching these sports for years, practicing with the same drills and routines and having success. Some may say, "if it ain't broke..." At the same time, all players and coaches are looking for the "the Edge"; the practice technique, game strategy, player development skill that will help the bottom line; winning. The physical training attributes still need to be developed in terms of raw speed, acceleration, agility, strength and balance. Hours are spent in the training rooms and gyms improving these variables. The game preparation process is still there; watching film, breaking down strengths and weaknesses of the opponent, tactical planning, etc. Some may say that is the "mental preparation" needed for competition. That's true, it is a plan for success, but the key is in execution of the plan. At the exact moment in the game when execution is needed, will each player know the right thing to do and be able to do it? That is the essence of what I call the "Sports Cognition Framework". It is the combination of the three themes: decision-making competence (knowing what to do), motor skill competence (being physically able to do it), and positive mental state (being motivated and confident to do it). There seem to be many, deep areas of research into each of these topics. My job is to dig into each of these areas and look for relevant research that you will find practical to include in your training or your coaching.

What Was He Thinking? Decision Theory in Sports

Previously, I outlined the core framework of sports skills. Over time, my intention is to dive deep into each of those areas and present research that will be useful to you in understanding the brain-body connection. Again, the goal of my ramblings here is to examine the foundation of skills necessary to perform well across the continuum of most sports. Ongoing posts will use this framework to organize this information into categories that are easy to search and focus on what you are interested in that day.

In addition to the core skills, there seems to be another equally significant side of sports cognition known as "decision theory". There is a deep research base in this area, not only specific to sports, but across other platforms (i.e. business, medicine, etc.) Basically, the application in sports looks at how athletes make thousands of split-second decisions during a game, some which will go unnoticed, but some that will affect the outcome. While most of these decisions appear instant and somewhat random, are there layers of "conditioning" that trigger one response versus another? Let's look at some examples:
Situation 1: Mike brings the basketball up the floor during a game and makes a pass to Tom. How many factors affected Mike's decision about that pass?
- Tom appeared to be "open".
- The play that the coach called dictated that Mike pass first to Tom.
- The game was tied and time was running out, and Mike knew Tom was the best option to score.
- Mike knew that Jack, another teammate, had missed his last 5 shots and wanted to avoid giving him the ball.
- Mike had missed his last 5 shots and was afraid to shoot.
- Mike and Tom are friends and feel the rest of the team is not at their skill level.
- Mike's choice was completely random
- Is there a "correct" answer, and if not, how do we judge effectiveness of the decision?

Situation 2: Mary is playing centerfield for her softball team. There are runners on 1st and 2nd base and there is 1 out. A ground ball is hit to her, she fields the ball and now needs to make a throw to a base. How does she decide where to throw?
- What is her "pre-pitch" analysis of the game situation? Does she have a plan of where to throw?
- What is the score of the game? Does she need to prevent a run from scoring?
- What is her self-assessment of her throwing ability? Does she have confidence in her throw to any base?
- What does her visual information give her during the play? When she fields the ball and looks up, what are her eyes telling her about the changing position of the runners?
- What are her teammates and coaches instructing (yelling at) her to do?
- Is there a "correct" answer?

To me, this side of the "80% mental" equation is just as important to success in sports. It deserves alot of attention and understanding, before we can coach athletes on how to improve these decision making skills. We will add this to our outline of research.