Author Archive

Shut the (Heck) Up About Sample Size

The analytics revolution in sports has led to profound changes in the way in which sports organizations think about their teams, players play the game, and fans consume the on-field product. Perhaps the best-known heuristic in sports analytics is sample size — the number of observations necessary to make a reliable conclusion about some phenomenon. Everyone has a buddy who loves to make sweeping generalizations about stud prospects, always hedging his bets when the debate heats up: “Well, we don’t have enough sample size, so we just don’t know yet.”

Unfortunately for your buddy, sample size doesn’t tell the whole story. A large sample is a nice thing to have when we’re conducting research in a sterile lab, but in real-life settings like sports teams, willing research participants certainly aren’t always in abundant supply. Regardless of the number of available data points, teams need to make decisions. Shrugging about a prospect’s performance, or a newly cobbled together pitching staff, is certainly not going to help the bottom line, either in terms of wins or dollar signs.

So the question becomes: How do organizations answer pressing questions when they either a) don’t have an adequate sample size, or b) haven’t collected any data? Fortunately, we can use research methods from social science to get a pretty damn good idea about something — even in the absence of the all-powerful sample size.

Qualitative Data
Let’s say you’re a baseball scout for the Yankees watching a young college prospect from the stands. You take copious notes about the player’s poise, physical stature, his hitting, fielding ability, and running abilities, as well as his throwing arm power. For instance, you might write things like, “good approach to hitting” and “lacks pure run/throw tool.”

All of these rich descriptions of this player are qualitative data. This observational data from one game of this college player is a sample size of 1, but you’ve got a helluva lot of data. You could look for themes that consistently emerge in your notes, creating an in-depth profile of the prospect; you could even standardize your observations on a scale from 20-80. Your notes help build a full story about the player’s profile, and the Yanks like the level of depth you bring to scouting.

You’ve worked as a scout for a few years, and the Yankees decide to bring you into their analytics department. It’s the end of the 2011 season, and one of your top prospects, Jesus Montero, just raked (.328/.406/.590, in 69 PAs) in the final month of the season. The GM of the Yankees, Brian Cashman, knocks on your door and says that they’re considering trading him. What do you say?

You compile all of Montero’s quantitative stats from the last month of the season and the minors, as well as any qualitative scouting reports on him. Good job. You’ve mixed quantitative and qualitative data to provide a richer story given a small sample of only 69 PAs. You’ve also reached the holy grail of social science research, triangulation, by which you examined the phenomenon from a different angle and, bingo, arrived at the same conclusion that your preliminary performance metrics gave you. Montero is a bum. Trade him, Brian.

Resampling Techniques
It’s four years later and Cashman knocks on your door again (he’s polite, so he waits for you to say, “come in”). It’s early October and you’ve just lost to the Houston Astros in a one-game playoff. Cashman asks you about one of the September call-ups, Rob Refsnyder, who Cashman thinks is “pretty impressive.” You combine Refsnyder’s September stats (.302/.348/.512, in 46 PAs), minor league stats, and scouting reports, but the data don’t point to a consistent conclusion. You’re not satisfied.

A fancy statistical method that might help in this instance is called bootstrapping; it works by resampling Refsnyder’s small 46 PA sample size over and over again, replacing the numbers back into the pool every time you draw another sample. The technique allows you to artificially inflate your sample size with the numbers that you already have. You can redo his sample of 46 PAs 1,000, 10,000, even 100,000 times, seeing each time how he would perform. Based on your bootstrapped estimates, you feel like Refsnyder’s numbers from last year are a bit inflated, but that he’d fit nicely as a future utility guy.

Cashman, who’s still in your office, now wants to know about two pitching prospects who were also called up in the 2015 class: James Pazos (5 IP, 0 ER, 3 H, 3BB, 5.4 K/9, 1.20 WHIP) and Caleb Cotham (9.2 IP, 7 ER, 14 H, 1BB, 10.2 K/9, 1.56 WHIP). If the team can only keep on of these pitchers, who should we keep? Who is better?

Normally you’d use a t-test to make comparisons between the two pitchers, but with such a small sample of innings for each guy, the conclusions wouldn’t be reliable. Instead, you decide to use a Mann-Whitney U test, which is basically the same thing as a t-test, adjusted for small samples. In fact, there’s a whole litany of statistical tests that are adept at handling small sample sizes: Wilcox’s t, Fisher’s exact, Chi-square, Kendal’s tau, and McNemar. You conclude that Pazos is slightly better, and that Cotham might be better suited for the bullpen. Cashman holds on to Pazos and deals Cotham to the Reds in the trade that brings over Aroldis Chapman to the Yankees. You pat yourself on the back.

Questions Need Answering
Having an adequate sample size brings confidence to many statistical conclusions, but it is certainly not a binary prerequisite for analyses. It’s easy for your buddy to watch his hindsight bias autocorrect for his previous wait-and-see approach, but organizations need to answer questions accurately. As amateur analysts and spectators, let’s change the lexicon by changing our methods.

Psychological Safety and the Adam LaRoche Saga

It was supposed to be the new cast of characters that stirred the pot on the south side. Who would have guessed that the preseason drama would emanate from an old war horse? The 36-year-old Adam LaRoche walked away from $13 million after White Sox management asked LaRoche to “dial it back a bit” and stop bringing his son Drake to the ballpark. Apparently, Drake had spent 120 games with the White Sox in 2015, and had already been a mainstay at the spring training facility in 2016.

Many of the White Sox players, including stars like Chris Sale and Adam Eaton, have publicly displayed their discontent with White Sox management, siding with both Adam and Drake LaRoche. Eaton was adamant enough to say that the White Sox “lost a leader” in Drake LaRoche [1] – a comment that he directed at team president, Kenny Williams. With so many players openly expressing their opposition to the removal of Drake LaRoche, it’s interesting to note that the issue arose from a small group of anonymous White Sox players who privately reported their distaste of Drake’s omnipresence.

Adam LaRoche was clear with his teammates – if there was ever an issue about his son Drake’s presence in the clubhouse, let him know about it:

Though I clearly indicated to both teams the importance of having my son with me, I also made clear that if there was ever a moment when a teammate, coach or manager was made to feel uncomfortable, then I would immediately address it. I realize that this is their office and their career, and it would not be fair to the team if anybody in the clubhouse was unhappy with the situation. Fortunately, that problem never developed [2].”

Unfortunately, things didn’t exactly play out that way, as no one brought it up to LaRoche personally:

Apparently, no one ever told LaRoche. These players and staff members didn’t feel comfortable even sharing it with their own teammates, with several White Sox players saying they never heard a complaint. But they did express their views to management [3].”

It’s not that LaRoche was an outcast. From the reaction of many of the players, it seems like primary players on the White Sox (if not large swaths of the team) were cool with Drake hanging around as much as he did. So, if a few people had a problem, why didn’t they speak up to LaRoche? Conversely, why couldn’t LaRoche sense that Drake was weirding some of his teammates out?

Let’s talk about feelings

 Average sensitivity is the ability of members within a team to sense how other team members are feeling by observing their facial expressions, body language, and other behavioral cues. Average sensitivity is an aspect of a broader construct called psychological safety, which helps to explain how and why team members speak up, exchange information, and their general willingness to be open with other teammates (Edmonson & Lei, 2014). There has been extensive research on the relationship between psychological safety and performance (Baer & Frese, 2003; Edmondson & Lei, 2014; Collins & Smith, 2006; Schaubroeck et al., 2011); broadly, this research indicates that open communication between team members is related to team performance.

How important is psychological safety in terms of group performance, you ask? Google’s Project Aristotle explored the characteristics that make the perfect team. After four years, hundreds of experimental teams, thousands of people, and 50 years’ worth of academic literature, the critical variable in predicting a successful team was…psychological safety [4].

Now, we haven’t measured the White Sox’ sensitivity or psychological safety directly, so the suggestion that these things played a role in the LaRoche situation is more of an educated guess than an empirical observation. Also, much of the research on the influence of psychological safety has been done in organizations, as opposed to sports teams. But it isn’t difficult to imagine that if there was a greater emphasis on psychological safety, a situation like this might not have arisen. It’s not too far of a jump to say that higher-performing teams should:

  • Place a premium on speaking up with ideas, without fear of punishment or ridicule. Don’t stifle effective collaboration regardless of the topic.
  • Promotion of safety is key – the research has shown that it does not arise naturally, but should be discussed and fostered.

Imagine if LaRoche’s teammates might have felt comfortable going directly to him instead of circumventing him. Would the White Sox still be in their current state of disarray? It could be less about this particular incident, and possibly more indicative of a greater, team-wide issue of communication.

Sooner or later, a situation similar to the Drake and Adam LaRoche situation is going to happen again. There’s also plenty of other team level constructs to explore, such as team chemistry. The broader point, though, is that these scenarios are likely somewhat avoidable: Teams can work to increase sensitivity and psychological safety. It won’t be easy, but research suggests that things like players-only meetings to air out grievances, establish lines of communication, and solidify roles might be a place to start. For the White Sox, it’s a rough way to begin the season, but fortunately these issues are fixable – and it’s certainly a helluva lot better to address these issues now rather than at the All-Star break.







The Sea Breeze Might Be Suppressing Homers at Petco Park

Land and water tend to do two different things when it comes to heat – the land retains it, while water repels it. The land’s retention of heat gives way by the afternoon, causing the rising heat to create a vacuum, which sucks in cooler air sitting on the surface of the ocean. Cool air rushes into the coasts by mid to late afternoon.

Petco Park is less than one mile from the Pacific Ocean, making it susceptible to these afternoon sea-breeze gusts, which tend to pick up in the spring time and fade in the summer. Fortunately, the ballpark is situated east of Coronado Island [1], which helps to buffer the would-be stronger sea breezes that might affect fly balls. The spring time gusts, the Coronado Island buffer, and the “effect” on fly balls are all hearsay. We’ll look closer at each of these, starting with the sea breezes at the ballpark.

The Wind Matters

Let’s take a closer look at how the wind affects fly balls at Petco Park. Not that the common word of the good people of San Diego can’t be trusted; it’s just a matter of science. Below is a graph of every home run hit at Petco Park over the last two years and the approximate wind speed while the home run was hit. It seems like there’s no correlation between wind speed and distance of home runs.

However, not all wind is created equal, so the directional changes of the wind might have some influence on the flight of the ball. In the 2014 and 2015 seasons, the directional path of the wind for 261 home runs was registered (the wind was either “calm”, “variable”, or “NNE” which registered in only one case).

Most home runs were hit while the wind was blowing in the west-northwesterly (WNW) direction. Given that center field is due north of home plate that would mean that a majority of wind is probably blowing over the Western Metal Supply Co. brick building. My guess (I’m not a meteorologist) is that the wind is drawn in from the ocean, over the top of Coronado Island. Here’s a bird’s eye view of Petco; the arrow indicates where the wind is coming from – it’s the WNW direction from home plate.

So, this begs the question: How does WNW wind affect the distance of home runs? If we only look at the 101 home runs hit while the wind was blowing from the WNW direction, we begin to see something going on (r = – .21, p = .04. For every 1.53 mph faster the wind blows from the WNW direction, 1 foot is lost from every home run hit (R2 = .04, p = .04, n = 101)

No other individual direction of wind registered a significant influence of the distance of home runs hit, nor did the combination of every other wind direction have any effect. So much for the Coronado Island buffer.

It’s a decent speculation that the direction in which a home run was hit (left, right, center) might be more or less affected by the WNW wind. However, the direction that the home run was hit had no effect on the relationship of the distance of the home run, with respect to the speed of the wind. Exit velocity (the speed of the ball off the hitter’s bat) is an obvious predictor of home run distance. Exit velocity did show the weakest correlation with home run distance when hit in the WNW direction as compared to every other direction [2]. It’s likely that lower exit velocity means that the home run hit spent more time spent in flight, and was thus more susceptible to WNW winds that suppressed its total distance, regardless of the direction that it was hit.

Addressing the hearsay

Wind direction and wind speed were recorded ten minutes before every hour of every home game for the last two seasons [3,4]. No surprise, WNW winds dominate during the course of every home game.

Wind speed does seem to be higher in the afternoon a compared to the evening, peaking in the late afternoon.

Additionally, May tends to have the strongest winds, but July and August have produced stronger winds than April. The theory that the spring is windier than the summer isn’t entirely true, but the spring does contain the windiest month of the regular season (May).

Why does this research matter?

Obviously, the pitcher and the batter are going to matter most. But, the WNW wind explains about 4% – 5% of the reason why the home run ended up where it did (R2 = .044). If you’re the Padres and you play 81 home games a year 4% – 5% might mean something to you [5].

Here’s a crazy idea: let’s say you’re the Padres and you’re playing an afternoon (3pm – 5pm) game and the winds are blowing in from the WNW (there are at least 22 home games this 2016 season that will be played between 3pm and 5pm). If it’s early in the game, start Carlos Villanueva, who has a career 40.4% FB%, and if it’s later in the game, use Jon Edwards who had a 67.6% FB% in 52 innings between AAA and majors last season. Meanwhile, give Matt Kemp a break (who has a career 36% FB%) and platoon rookie Travis Jankowski who showed a 27% FB% in 34 games last year with the Padres.


Why did I only choose the last two years? Wind patterns and sea breezes can change over time [6]. If we rewind the years, we may or may not see similar results. I felt that the last two years were a decent idea about what we could expect from 2016, any further back, and I might have run into a different profile. Don’t agree with these results? Add a few years, and let’s see if the trend holds — I’m all for more objectivity.

Yes, sea breezes could entail the “marine layer” which brings a body of cool and moist air into the ballpark, and I might take a look at that with my next article. However, it’s not the moisture that will suppress home runs — it’s the cool air. Warm air expands and lowers the air density, which results in less resistance on the baseball. Therefore the cooler the air is, the higher the density. Water (H2O) is less dense than atmospheric O2 and N2, therefore if there’s more moisture in the air, we’d see less resistance on the baseball [1]. Temperature, dew point, humidity, and pressure had no effect on the distance of home runs between 2014 and 2015.


[2] Of the 4 directions that reported significant effects: North Northwest (r = .674, p < .01, n = 16), Northwest (r = .473, p < .01, n = 45), West Northwest (r = .393, p < .01, n = 101), West (r = .591, p < .01, n = 36)



[5] Quality of batter and/or pitcher was not tested in a multiple regression model, nor were any other predictor variables beyond wind speed. 

[6] See Coors Field effect:

Mostly Useless Information About the World Series In the Wild Card Era

We could easily call my decision to publish an article with playoff predictions using a brand-new theory about previous success predicting future success ballsy (or stupid). To summarize, research by Rosenqvist and Skans (2015) [1] showed that golfers who barely qualified for a golf tournament would go on to have more success in future tournaments than golfers who barely missed the cut in the same tournament. Seemingly accidental success created confidence, which led to more success in the future. So, using this logic, I wanted to see if this same phenomenon occurred at the team, rather than the individual level. The attempt was to predict all divisional victors from this year’s 2015 MLB playoffs using previous playoff experience and success as the predictor. As it turns out, the teams with more experience/success were only 1 for 4 in the first round of the playoffs.

This time, instead of making predictions, I did the smart thing and looked at previous trends. Instead of using the first round of the playoffs (which arguably is more erratic given that it’s only a five-game series), I focused solely on the World Series. I totaled all the previous playoff experience, age, and WAR for every player on each 25-man World Series team roster in the Wild Card Era (1995 – 2015, n = 42 teams).

WAR doesn’t predict the winner of the World Series

Is this old news? I don’t know. Tallying up a team’s WAR correlates with the actual number of wins that team will have by the end of the regular season (somewhere around r = .82 last time I checked), but it doesn’t correlate with the victor of the World Series. In fact, 13 out of the last 21 (62%) World Series victors had average WARs lower than their opponent’s.

Differences in experience at the team level relate to the duration of the World Series

The difference in previous playoff experience between the two World Series teams is a good predictor of the number of World Series games that will be played in a series. Specifically, at the team level, the greater the difference in the average previous playoff series won (r = -.45, p < .05, n =21), the average number of World Series appearances (r= -.45, p < .05, n =21), and the average number of World Series titles (r = -.46, p < .05, n =21) between the two teams, the less World Series games played that year. You’re saying, “yeah but what about the 2014 World Series that went 7 games when the seasoned Giants played the inexperienced Royals?” It’s just a trend, not a guarantee.

Other tidbits

  • The higher the average of previous World Series appearances across both World Series teams, the higher number of television viewers (r = .45, p < .05).
  • The World Series victor with the highest average WAR per player was the 1998 Yankees (m = 2.57); the lowest WAR was the 2006 Cardinals (m = 1.26).
  • Oldest World Series victors were the 2000 Yankees (m = 30.7); youngest were the 2002 Angels (m = 27.4).
  • Most experienced victor was also the 2000 Yankees (96% of the team had previous playoff experience), and least experienced were the 2002 Angels (0%).

More needs to be understood about this theory

There was however, no relationship between previous playoff experience and that year’s World Series outcome. In terms of playoff experience, the results from Rosenqvist and Skans could not be replicated in this setting. Baseball isn’t golf, and baseball isn’t an individual sport, it’s a team sport. Perhaps the average and/or aggregate levels of experience within a team might manifest differently than for an individual. So, too, are there other ways to operationalize this hypothesis of previous experience/success, so I wouldn’t write this off as a done deal. We’re still a long ways away from determining how and if this theory occurs within the context of baseball – more research into the theoretical underpinnings is always the answer.

Back to the drawing board.

[1] Rosenqvist, O. & Skans O.N. (2015). Confidence enhanced performance? – The causal effects of success on future performance in professional golf tournaments. Journal of Economic Behavior & Organization, 117, 281-295.

Measuring Team Chemistry with Social Science Theory

Every athlete, professional or otherwise, talks about that feeling of being on a team. There’s something that happens when a team “clicks” – it’s a united feeling of team spirit that propels team members to compete, most often referred to as team chemistry. In the social sciences there’s no measure of team chemistry, but there is however Team Cohesion, which is defined as:

A dynamic process that is reflected in the tendency of a group to stick

together and remain untied in the pursuit of its instrumental objectives

and/or for the satisfaction of member affective needs [1].

Team cohesion has been shown to exist across multiple work group settings (organizational, military and sport) [2], as well as across multiple sports (basketball, golf [3], softball, and baseball [4]). Perhaps more interestingly, cohesion has also been bi-directionally linked to performance: when teams perform better, they are more cohesive; and when they are more cohesive, they perform better [2,5]. And while the research on this relationship is clear, it has mostly been conducted with non-professional teams. Indeed, team cohesion is one of many other “unobservable” properties that are untapped within profession sports.

How can we measure team cohesion in professional sports?

 As researchers, we would normally use a validated survey to measure team cohesion – a survey that I could rely on to accurately measure team cohesion. Unfortunately, when I don’t have access to a team, I’m forced to use alternative methods. The first step is to examine the literature; a few key findings are brought to light about indications of team cohesion:

  • Team cohesion is related to the extent that members accept the roles on their team (captain, motivator, leader, follower, etc.) [6].
  • Charismatic leaders will refer to their teams more often than referring to themselves [7].
  • The higher the level of team cohesion, the better the team performance [2,5].

So, if I can somehow measure how often leaders refer to their teams (vs. themselves), then I can use this as an approximation of their leadership characteristics. And if leaders are acting like leaders, they may also be helping to solidify roles within their team. Therefore we might expect that:

Hypothesis 1: As leaders reference their team more, we should see increased team cohesion – and as team cohesion increases, we should see better performance.

A charismatic leader does not typically arise without a contextual or conditional trigger. Crisis often prompts the emergence of charismatic leadership – a setting that allows a charismatic leader to propose an ambitious goal [8]. Both the context and the charismatic leader influence one another, almost as if the leader requires crisis as an occasion to exemplify charismatic leadership [9]. Additionally, at the group level, team members have been shown to become more attached to the leader in times of crisis, prompting a greater presence of cohesion during times of crisis as followers rally around the charismatic leader [10].

In baseball, teams experience all types of crises throughout the long season, including injuries, losing streaks, playoff races, and team conflicts. Perhaps the most common and least contextual of these crisis is the race to the playoffs as the season comes to an end. With an understanding of how and when the playoff races begin to make an impression, I can expect to observe a temporal effect of charismatic leadership by using our previous indicator of team reference. That is, it may not only be that “there is a positive relationship between a leader’s team references and the amount of wins his team will have at the end of the regular season”, but also:

Hypothesis 2: The timing of when a team leader references his team can determine the effectiveness of his leadership.


As the first component of the measure, I needed to assess team leaders’ reference to themselves or their team, I used the most popular newspaper from that team’s city to extract quotations (e.g., San Francisco Chronicle for the Giants; the New York Times for the Yankees). A team leader was identified by teammates, coaches, or front offices as a “leader”, a “captain”, or having either of these qualities. If there was more than one identified team leader, I randomly chose between the two. I tracked the quotes from 8 randomly selected baseball team leaders from 8 randomly selected teams across an entire regular season (April 4th, 2012 – October 3rd, 2012). Statement settings included comments made in locker rooms after games, during the All-Star break, before a game started, or in any other setting. Any time the leader was documented as saying anything that appeared in the newspaper, that quote was documented for analysis. Leader quotes were qualitative coded independently between 3 different coders. Each quote was coded as containing “self-reference”, “team-reference”, and/or “other reference” (the 3 coders had 97% agreement on their final codes). I began this study in 2013 thus I used the 2012 season, which was the latest complete season at my disposal.

Due to the disparity in responses, the sample was aggregated based on team leaders who played on teams that finished with a certain number of wins. Since 1996, no AL team has made the playoffs with less than 86 wins [11]. During the same time period, no NL team has made the playoffs with less than 82 wins [12]. For this study, leaders were categorized based on how their teams finished the regular season (86 or more wins for AL teams and 82 or more wins for NL teams). Those at or above the win mark were titled “high team leader” (HTL) and those below the win mark were titled “low team leader” (LTL). Four teams in the sample met the HTL criteria and their combined record was 368 – 280 (.568 wining percentage). Not all HTLs were on teams that made the playoffs in 2012, but each of the four teams were competing for a playoff spot in the months of August and September. Four teams in the sample met the LTL criteria and their combined record was 296 – 352 (.457 winning percentage).


High or low team leader classification

Team League 2012 Regular Season Record Team Leader High or Low Team Leader
Angels AL 89-73 Torii Hunter HTL
Giants NL 94-68 Buster Posey HTL
Yankees AL 95-67 Derek Jeter HTL
Rays AL 90-72 Evan Longoria HTL
Rockies NL 64-98 Michael Cuddyer LTL
Twins AL 66-96 Justin Morneau LTL
White Sox AL 85-77 Paul Konerko LTL
Phillies NL 81-81 Jimmy Rollins LTL
     Table 1. Classification of high or low team leaders based on their team’s 2012 regular season record


There was no significant correlation between the total number of team references and the total number of wins that a leader’s team had at the end of the regular season r = .237, p > .05). Nor was there an indication of a negative correlation between self-references and total number of team wins r = -.086, p > .05.

Leader responses were then aggregated between LTLs and HTLs. Of the 490 total responses, 252 responses were made after or in reference to a previous game. Quotes were then selected for these post-game interview responses after a leader’s team had won a game (162 total) or lost a game (90 total). After a loss, both HTLs and LTLs referred to their teams much more often than referring to themselves. LTLs were 7.20 times as likely to reference their team after a loss than reference themselves. When compared to LTLs, HTLs were less likely to refer to their team after loss (4.42:1). After a win, LTLs were 1.41 times as likely to reference their team than themselves. HTLs on the other hand were 2.32 times as likely to reference their team than themselves after a win (Table 1).

Reference to team or self as ratio

Leader Loss Win
HTL 31:7 (4.42:1) 65.28 (2.32:1)
LTL 36:5 (7.20:1) 45:32 (1.41:1)
     Table 2. Ratios of team vs. self references for each type of leader

The monthly distribution of team reference for LTLs was relatively even across all months of the regular season. The highest percentage was July (19.9%) and the lowest was August (12%), a difference of 7.9% (Figure 1). The overall standard deviation for team references by month was σ = 2.88. In contrast, team reference for HTLs was much more dynamic. The highest percentage was September (39.6%) and the lowest was June (5.8%), a difference of 33.8%. September team references for HTLs were more than double any other month. The overall standard deviation was σ = 12.2, with the resulting distribution becoming much more parabolic (Figure 2). The quadric trend line that is used to represent the team reference distribution for HTLs showed a very good fit R2 = .91.

nullFigure 1. Percentage of team reference by month LTLs
           Figure 2. Percentage of team reference by month HTLs with quadratic trend line



The increased rate of team reference by HTLs as compared to LTLs may have helped to establish better role clarity – a characteristic of more cohesive teams. This was further marked by the fact that HTLs were on higher performing teams than LTLs. The direction of the team cohesion to performance relationship in this case is still unknown.

HTLs also referred to their teams most often during the end of the regular season. This relates to the theory that charismatic leaders will “activate” in times of crisis. In turn, this helps to create more team cohesion as members attach themselves to leaders in times of crisis.


[1] Carron, A.V., Colman, M.M., Wheeler, J., & Stevens D. (2002). Cohesion and Performance in Sport: A Meta Analysis. Journal of Sport & Exercise Psychology, 24, 168-188.

[2] Mullen, B. and Copper, C. (1994). The relation between group cohesiveness and performance: an integration. Psychological Bulletin.115, 210-227.

[3] Vincer, D., & Loughead, T.M. (2010). The Relationship Among Athlete Leadership Behaviors and Cohesion in Team Sports. The Sport Psychologist, 24, 448-467.

[4] Carron, A.V., Bray, S.R., & Eys, M.A. (2002). Team Cohesion and Team Success in Sport. Journal of Sports Sciences. 20(2). 119-126.

[5] Oliver, L.W., Harman, J., Hoover, E., Hayes, S.M., & Pandhi, N.A. (2003) A quantitative integration of the military cohesion literature. Military Psychology, 11, 57-83.

[6] Carron, A. V., & Eys, M. A. (2012). Group dynamics in sport (4th ed.). Morgantown, Fitness Information Technology.

[7] Shamir, B., Arthur, M.B., & House, R.J. (1994). The rhetoric or charismatic leadership: A theoretical extension, a case study, and implications for research. The Leadership Quarterly, 5(1), 25-42.

[8] Poon, J. & Fatt, T. (2000). Charismatic Leadership. Equal Opportunities International. 19(8), 24-28.

[9] Conger, J. A. (1999). Charismatic and transformational leadership in organizations: An insider’s perspective on these developing streams of research. The Leadership Quarterly, 10, 145-179.

[10] Kets de Vries, F. R. (1988). Prisoners of leadership. Human Relations, 41, 261-280.

[11] Gaines, C. (2011, April 21). Chart of the Day: What it takes to make the playoffs in Baseball. Business Insider. Retrieved from what-it-takes-to-make-the-playoffs-in-baseball-2011-4

[12] Bloom, B.M. (2005). Padres Try to Recover from 82-80 Record. San Diego Padres. Retrieved from

What If Prior Playoff Success Were the Only Thing that Mattered?

Ed. note: this was probably intended for a few days ago, but it just showed up, so, enjoy!

Determining playoff success ain’t like predicting outcomes during the regular season. Smaller sample sizes, emotions, momentum, and magical realism have been blamed for seemingly unexplainable outcomes in baseball’s postseason. Common knowledge about predicting success doesn’t add up, “shouldn’t the best teams win the World Series every year?” It might depend on what we call, “best”.

It’s not always the best regular-season teams that win the World Series; in the last 20 years only four teams that had the best record in baseball have gone on to win the World Series (20%). Even momentum heading into the playoffs doesn’t seem to amount to much World Series success either; the 10 best September records heading into the playoffs haven’t amounted to a World Series victory during the Wild Card Era[1].

Despite these results we still can’t get away from favoring the best teams every single year — after all, “nothing succeeds like success.” There is something to be said about previous playoff success within the wild-card era, and whether it is maintaining the rosters of successful teams or a cultural revitalization within these teams, previous playoff success has paid off. In fact, 13 of the last 20 years of World Series championships (65%) belong to only 4 teams: Yankees, Red Sox, Cardinals and Giants.

A new study on previous success by Rosenqvist and Skans (2015)[2] may have shed some light onto this phenomenon. Their experiment compared golfers of seemingly equal skill and ability: golfers who marginally made the cut for a golf tournament vs. golfers who marginally missed the cut for the same tournament. They found that golfers who made the cut showed an increase in performance in subsequent tournaments compared to those golfers who missed the cut. Early luck leads to increased confidence, which later leads to more success.

Success, either accidental or otherwise, seems to be contagious. Baseball, however, isn’t golf (unless you’re Brandon Belt). Baseball is comprised of teams of individuals, each with their own history of success or failure, confidence or doubt. However, using this theory, could it be the case that teams that are comprised of players with more playoff success have the confidence to do it again?

I totaled all of the playoff experience for every player on the 8 playoff teams in the ALDS and NLDS. To have contributed to previous playoff success, a player had to have played at least once during a previous playoff run (pitched at least one pitch, come in to pinch-run, come in to play defense, or taken at least one at-bat). Below, each team has an “average player profile” that defines each team’s average postseason player. The profile is comprised of the average experience and success across five variables: years that a player has contributed to a playoff team, total playoff games won with each contributed team, playoff series won with each contributed team, World Series appearances with each contributed team, and World Series victories with each contributed team.

Kansas City Royals

Average years contributed Average playoff games won Average playoff series won Average World Series appearances Average World Series victories

Average age


8.16 2.08 0.72 0.08


Though the 2015 Royals have carried their 2014 playoff experience with them, it’s not last year’s remaining players that are most intriguing. In some savvy acquisitions the Royals have padded their already experienced squad with some playoff warhorses. The 2015 acquisitions of Joba Chamberlain, Ryan Madson, Franklin Morales, and Jonny Gomes all come with some serious playoff success – each have a World Series ring.

Yet, despite the playoff experience added by this year’s Royals, their 2015 playoff roster doesn’t include Joba Chamberlain or clubhouse glue-guy Jonny Gomes, each with a ring. Omar Infante was also left off, who had the second-most World Series appearances across all 8 teams, tied with Matt Holliday with 3. Despite the youth-driven movement from last year’s team, the 2015 Royals are surprisingly the second-oldest team in this year’s postseason. Their age comes with some success – the average 2015 Royal has been to the playoffs, won an average of 8 games, won at least 2 series, and been to a World Series.

Houston Astros

Average years contributed Average playoff games won Average playoff series won Average World Series appearances Average World Series victories

Average age


1.56 0.28 0.04 0.00


Young teams with no playoff experience can play like they have nothing to lose; they’re young, they’re talented, and there’s a belief that if they’re this good now, they’ll be able to make the playoffs again in the future. It runs counter to the success-confidence-success theory, but this could be the story for the 2015 Astros who could propel themselves to an accidental World Series appearance.

The only player on the 2015 Astros to have been to a World Series is Scott Kazmir with the 2008 Rays. Overall, the pitching staff is older (m = 30.1 years old) and more successful (m = 2.27 games won) compared to their position players (m = 26.9 years old, m = 1.00 games won). This composition is counter to the 2015 Mets, who have pitching youth paired with position-player experience. The Astros are a young team; they’ll be looking to pull a 2014 Royals on the 2015 Royals.

Royals in 5

Toronto Blue Jays

Average years contributed Average playoff games won Average playoff series won Average World Series appearances Average World Series victories

Average age


3.72 0.80 0.24 0.00


When the majority of a team’s playoff experiences comes from a duo of former Rockies, you know two things: 1) It’s been a long time since the Blue Jays have reached the playoffs and 2) the current team lacks playoff experience. The only player who knows what it takes to win a World Series is Mark Buehrle, and apparently his late-season implosion was enough to leave him off the postseason roster. The Blue Jays sport the second-oldest average player (m = 29.3) including the oldest player in this year’s postseason to be in the playoffs for the first time – the 40-year-old R.A. Dickey. The Blue Jays join the Mets and the Astros to field a team without any players who have won a World Series.

This is the opposite of playing with accidental confidence, where a young or inexperienced team suddenly finds themselves in the playoffs and plays the game like they’ve got nothing to lose, “there’s always next year”. Well for these Blue Jays, next year isn’t a guarantee. They may not be playing with a blithe spirit of reckless abandon but the fleeting dreams of older players who may never reach the playoffs again. But who knows, maybe the exuberance of being in the playoffs for the first time is enough to spark a youthful movement? The theory disagrees.

Texas Rangers

Average years contributed

Average playoff games won Average playoff series won Average World Series appearances Average World Series victories

Average age


7.28 1.56 0.64 0.08


The average Texas Ranger profile is a bit deceptive – heavily weighted by those that have previously been to the playoffs. The average Texas Ranger who has previously been to the playoffs has won 15.2 games, has won 3.9 playoff series, and has been to almost 2 World Series (m = 1.78). In fact 36% of the 2015 Rangers’ postseason roster have been to a World Series. The Rangers have very quietly maintained many of the players who got them to the back-to-back World Series’ in 2010-2011 (Lewis, Holland, Moreland, Andrus, Napoli, Hamilton).

This team will be overlooked for their lack of pitching, but their postseason success cannot be ignored. With the average Texas Ranger having nearly double the success of winning playoff series than the average Blue Jay, we might expect this series to be a cakewalk for the Rangers. Then again, it’s a five-game series, and the Blue Jays have some serious star power.

Rangers in 4.

Chicago Cubs

Average years contributed

Average playoff games won Average playoff series won Average World Series appearances Average World Series victories Average age
0.92 3.76 0.92 0.20 0.12


In “the year of the rookie” it only makes sense to have two young teams representing each league in the postseason. If you removed Austin Jackson, the 2015 Cubs start to look a lot like the 2015 Astros. The pitching staff is older (m= 29.9 years old) and more successful (m = 4.73 games won) compared to the Jacksonless position players (m = 27.1 years old) and (m = 1.92 games won). The Cubs are lucky to have both a position player (David “Dad bod” Ross) and a pitcher (Jon Lester) with World Series rings, along with 16% of postseason players with World Series exposure. So, maybe the Cubs are a slightly more seasoned version of the 2015 Astros.

The Cubs are also deceptively successful in the playoffs. Despite an average of only 1 year in the playoffs, the average Cub has won almost 4 games and 1 series. Compare this to the New York Mets who have relatively the same amount of experience, but with far less success.

Between the minds of Theo Epstein and Joe Maddon are some great ideas about utilizing leadership, team chemistry, and plenty of other intangibles. Count on the Cubs to take advantage of the balance between youth and experience during this year’s playoffs.

St. Louis Cardinals

Average years contributed

Average playoff games won Average playoff series won Average World Series appearances Average World Series victories Average age
2.64 14.6 3.56 0.84 0.32


The average Cardinal has some serious playoff success. The average Cardinal has been to the playoffs at least 2 years, won at least 14 games, and won at least 3 series. The 2015 postseason Cardinal has not only been to the World Series, but 25% of the 2015 postseason Cardinals have won a World Series; all of this playoff success and still a relatively young team. The Cardinals have the 2015 player with the most postseason experience in Yadier Molina, who has appeared in 4 different World Series and won 2 of them. The saddest part about the 2015 Cardinals is the absence of Randy Choate, who won a World Series with the 2000 Yankees (2 World Series rings in 16 years would have been a cool story).

The Cubs might give the Cardinals some fits, but the theory says that the Cardinals shouldn’t have a problem disposing of the Cubs. Count on the Cardinals making it back to the World Series.

Cardinals in 4

New York Mets

Average years contributed

Average playoff games won Average playoff series won Average World Series appearances Average World Series victories

Average age

0.96 2.32 0.40 0.04 0.00


If there are counters to the success-confidence-success theory it’s the 2015 Mets, who are basically the 2010 San Francisco Giants: loaded with young talented pitching and complemented with older, experienced position players. The Mets are in fact the youngest of the 8 playoff teams, though their youth comes with a price. In fact, the Mets’ pitching staff is so young and inexperienced, if you removed Bartolo Colon, you’d only have 1 pitcher with playoff experience (Tyler Clippard with the hapless 2012 and 2014 Nationals).

Quite similar to the 2010 Giants, the only player on the 2015 Mets to have won a World Series is Juan Uribe (2005, 2010) who did so with the Giants in 2010, yet due to injuries isn’t on the playoff roster. The Mets will have some decent playoff success with Curtis Granderson, David Wright, and Michael Cuddyer who can describe to young players what it’s like to lose in the playoffs. The only player to have even been to a World Series is Granderson when he was on the Tigers who lost to the Cardinals in 2006.

Los Angeles Dodgers

Average years contributed

Average playoff games won Average playoff series won Average World Series appearances Average World Series victories Average age
2.04 6.64 1.28 0.24 0.08


Chase Utley + Jimmy Rollins + Good Starting Pitching = 2007-2011 Philadelphia Phillies. The Dodgers are hoping they get the 2008 version, though by the looks of things, it may resemble more of the 2010 version. The average Dodger has been to the playoffs, won a few games, and won at least 1 series. It’s really a smattering of success and experience despite being the oldest team in 2015 postseason (m = 29.6 years old).

Their playoff success says that the Dodgers should be able to handle the Mets, though the real test will be whether this older group of players will take to the leadership and previous success of Utley and Rollins. If the Dodger players are smart, they’ll humble themselves as much as possible, hone in, and play as a team.

Dodgers in 4


Conclusion and Caveats

  1. Yes, skill and ability is obviously something to take into consideration. Though, players who are highly skilled tend to find themselves on more successful teams, so the two may be related. The same can be said for age: the older you are, the more playoff experience you’re likely to have. However, if you look at this year’s teams, average age and average playoff success don’t seem to be related at all.
  1. Yes, in recent memory, the 2010 Giants and the 2014 Royals have been successful in the postseason despite a lack of playoff experience and success. However, in the playoff era, how many have actually won the World Series? Few come to mind.
  1. Yes, skill seems to be valued more that experience. Most managers tend to stick with their highest-performing players, and you can’t blame them. However, if this theory holds true, maybe you can blame them. The second season might benefit from previous playoff success. The counter to this is to picture a 2015 postseason team with 67-year-old Johnny Bench, 84-year-old Willie Mays, and “Mr. October” 69-year-old Reggie Jackson (all have some serious playoff success, right?). Recall from #1 that skill and ability obviously count, but the theory states that previous success might count too.



[2] Rosenqvist, O. & Skans O.N. (2015). Confidence enhanced performance? – The causal effects of success on future performance in professional golf tournaments. Journal of Economic Behavior & Organization, 117, 281-295.

Analytics Are Good, But Psychometrics Can Make Them Great

This is not about a relief pitcher resting horizontally on a comfy couch as he spills his deepest darkest secrets to a furrowed, bearded psychologist, nor is this about prescribing medication to a team’s severely depressed kicker who just missed the game-winner. We’re talking about sports psychology, but not the kind of stereotypical psychology you’re used to. Instead, we’re talking about psychometrics – how to measure the ways that a player’s psyche (thoughts, feelings, opinions) relates to the most important thing imaginable for sport teams: performance.

Seeing is believing

Counting the yards that a running back gains after contact or the runs prevented by pitching independent of defense are advanced numerical methods of breaking down a player’s performance. Most of the traditional analytics work the same way; a player’s previous performance is charted, observed, and dissected to make a projection about how that player will perform in the future. A team’s forecasted performance is usually the sum of the individual players’ projected performances. This is (generally) the state of analytics in a nutshell.

Not only have analytics shown that previous performance predicts some level of future performance, it also just makes sense. Watching a player hit a 3-point shot, scoring pad-side against the goalie, and hitting a home run are visible to everyone; it’s what makes sports, sports. You know that Mike Trout is a good baseball player because you can see his performance. You can see him make ridiculous plays in the outfield and then watch him hit a home run into a fishing net in the center-field bleachers. You can check the box score the next day and you can see the numbers immediately reflect his awesomeness. You can visit FanGraphs and read about a sabermetric stat that further corroborates Trout’s awesomeness, and then you can use that same stat to find out about another obscure player’s performance and realize he’s kind of awesome as well. Analytics makes sense because most of it is overtly visible – above the surface, leaving everything else that can’t be seen as “intangible”.

What lies beneath

 Even if analysts were to measure more “intangible” characteristics, like a player’s leadership, grit, or mental toughness, they don’t seem to amount to the same numerical accessibility as traditional performance metrics, nor do they seem to be relatable to future performance. However, with carefully designed tools, psychometrics can not only measure these “intangible” characteristics, but can help predict future performance in the same way as traditional analytics. Ideally, psychometrics from players and teams can help complement performance analytics that are now readily being used.

In fact, measurement of the human mind and behavior isn’t anything new – over 100 years of psychological research has shown that the human psyche is quantifiable in the same way that previous performance is quantifiable. Psychologists have measured and quantified aggression across different cultures[1], charismatic leadership in managers[2], intrinsic motivation in children[3], and team cohesion within collegiate and recreational sports teams[4]. What’s more, these numbers can even fit nicely into the same models, projections, and predictions that have been used with traditional analytics. Yet despite the depth and breadth of this research, professional sports teams have been slow to tap into this area of study, pooh-poohed by pundits as “intangibles,” unseen and unrecognized by professional sport team brass.

You won’t know unless you try

If the results of these measurements help to win more games, what do teams have to lose? Teams should not fear the minuscule amount of time that their players would spend filling out a carefully designed survey if it means understanding more about them – and, ultimately, understanding more about their team. Teams should not fear the analysis of dugout, sideline, team bus, or hotel conversations between players, all of which include rich amounts of data that can help to explain the relationships between players. Teams should not fear the measurement of a player’s comments, quotes, tweets, or posts, their spoken or written words might reveal hidden emotions or intentions. The analytics movement is far from over, and if teams are looking for more numerical insights, look no further than psychometrics.


[1] Ramirez, J.M., Fujihara, T., & Van Goozen, S. (2001). Cultural and Gender Differences in Anger and Aggression: A comparison between Japanese, Dutch, and Spanish students. Journal of Social Psychology. 141, 119-121.

[2] Conger, J.A., Kanugo, R.N., & Menon, S.T. (2000). Charismatic leadership and follower effects. Journal of Organizational Behavior. 21, 747 – 767.

[3] Marinak, B.A. & Gambrell, L.B. (2008). Intrinsic motivation and rewards: What sustains young children’s engagement with text? Literacy Research and Instruction, 47(1), 9 – 26.

[4] Carron, A.V., Colman, M.M., Wheeler, J., & Stevens, D. (2002). Cohesion and performance in sport: A meta analysis. Journal of Sport and Exercise Pscyhology. 24, 168 – 188.