Beyond the Box Score is a fantastic site, examining baseball from an analytical perspective.  The authors definitely embrace sabermetrics, but they don’t beat readers over the head with complex statistics.  As with most things that I do, the subject of my columns will vary quite a bit.  Generally speaking I’ll likely focus on team performance, player valuation, and lots of exploratory questions about the game.  Oh, and you can be sure there will be lots of pretty visuals and laments about the NY Mets.

Be sure to stop by if you are interested.  You can read and subscribe to my entries here, but I encourage you to subscribe to the site as a whole (RSS feed here).

2010 marks the end of the “ought” decade for Major League Baseball.  I thought I would take the opportunity to analyze the last 10 years by visualizing team data.  I used Tableau Public to create the visualization and pulled team data from ESPN.com (on-field statistics) and USA Today (team payroll).

The data is visualized through three dashboards.  The first visualizes the relationship between run differential (RunDiff) and OPS differential (OPSDiff) as well as the cost per win for teams.  The second visualization looks at expected wins and actual wins through a scatter plot.  The size of each team’s bubble represents the absolute difference between their actual and expected wins.  Teams lying above the trend line were less lucky than their counterparts below the trend line.The final tab in the visualization presents relevant data in table form and can be sorted and filtered along a number of dimensions.

The first visualization lists all 30 teams and provides their RunDiff, OPSDiff, wins, and cost per win for 2001-2010.  The default view lists the averages per team over the past 10 years, but you can select a single year or range of years to examine averages over that time frame.  The visualization also allows users to filter by whether teams made the playoffs, were division winners or wild card qualifiers, won a championship, or were in the AL or NL.  The height of the bars corresponds to a team’s wins (or average wins a range of years).  The color of the bars corresponds to a team’s cost per win–the darker green the bar the more costly a win was for a team.  Total wins (or average for a range of years) is listed at the end of each bar.  In order to create the bar graph I normalized the run and OPS differentials data (added the absolute value of each score + 20) to make sure there were no negative values.  For the decade, run differential explained about 88% of the variation in wins and OPS differential explained about 89% of the variation in run differential.

The visualization illustrates the tight correlation between RunDiff and OPSDiff, as the respective bars for each team are generally equidistant from the center line creating an inverted V shape when sorted by RunDiff.  In terms of average wins over the decade, there are few surprises as the Yankees, Red Sox, Cardinals, Angels, and Braves round out the top 5.  However, St. Louis did a much better job at winning efficiently, as they paid less per win than the other winningest teams (<$1M per win).

(click for larger image)

The viz also illustrates the success of small market teams such as Oakland and Minnesota who both averaged roughly 88 wins while spending the 3rd and 4th least respectively per win.  If you filter the visualization for teams that averaged over 85 wins during the decade, it really drives home how impressive those two teams’ front offices have been at assembling winning ball clubs with lower payrolls.  No other team that averaged >85 wins paid less than $975K per win.  Oakland looks even more impressive when you isolate the data for years that teams qualified for the playoffs.  Oakland averaged 98.5 wins during seasons they made it to playoffs, and did so spending only $478K per win.

(click for larger image)

What about the big spenders?  The five biggest spenders included the Yankees, Red Sox, Mets, Dodgers, and Cubs.  The Yankees spent an astounding $1.8M per win during the decade, but they also averaged the most wins with 97.  Some will say this provides evidence that the Yankees–and other big market teams–simply buy wins and championships.  However, only 17% of the variation in wins was explained by payroll during the decade.  Moreover, while the Yankees occupied 6 of the top 10 spots in terms of cost per win they were the only team to earn a positive run differential.  The Cubs, Mets, Mariners and Tigers all finished under .500 and missed the playoffs while those Yankee teams qualified for the playoffs 5 out of 6 years and won one World Series.  Yes, the Yankees spend significantly more per win, but they spend more wisely than many other deep pocket teams.

Teams that made the playoffs averaged a little over $1M per win in those years they qualified, with Wild Card teams ($1.030M) spending a tad bit more than Division winners ($1.006M)–about $14K per win on average.  World Series winners spent $1.08M per win in their winning years compared to $1.002M for other playoff teams.  Teams that failed to make the playoffs averaged $923K per win.

The best team of the decade in terms of run differential?  The 2001 Seattle Mariners, who amassed an incredible +300 RunDiff.  Even with that total they were only expected to win 111 games–they would go on to win 116.  The Mariners had only the 11th highest payroll that year and so paid a measly $644K per win.  The absolute worst team of the decade?  The 2003 Detroit Tigers, who earned a RunDiff of -337 and actually won less games than expected (43 vs. 47).  Given their ineptitude on the field, the Tigers paid $1.14M per win even though their total payroll for the year was only $49M.

Luckiest team?  The 2005 Diamondbacks who won 77 games despite a RunDiff of -160 (only 64 expected wins).  Hardest luck team?  The 2006 Indians, who only won 78 games with a +88 RunDiff that should have translated into 90 wins.

(click for larger image)

There are tons of ways to manipulate the visualizations and cut the data.  Hopefully viewing the data in this way is helpful and illuminates some things we didn’t know and drives home other things we had a hunch about. This is my first attempt to visualize this data, so please feel free to send along any and all comments so I can improve it.

Author’s Note: Due to a very helpful comment by Joshua Maciel, I have updated the visualization.  Here is a link to the original version for those that are interested.

While Feller was a Hall of Fame player his performance relative to other greats can be debated.  Certainly, Feller had impressive traditional statistics.  He averaged close to 15 wins a season over 18 years and had a .621 winning percentage.  Had he not lost those prime years to the war he very well could have amassed between 340 and 350 wins for his career.  He also averaged 143 strikeouts per season, finishing with almost 2600 for his career, leading the league seven times and striking out an amazing 348 batters in 1946.  However, he also had the 5th most walks allowed in history (1764), walking an astounding 208 batters in 1938.  His penchant for walks earned him a career WHIP (Walks + Hits per Inning Pitched) of 1.32, good for 527th all time, easily one of the worst for a Hall of Fame pitcher.  Despite having a remarkably powerful arm, he finished his career with a strikeout-to-walk ratio of only 1.46, 650th all time, again one of the worst for a Hall of Famer.

But despite a high WHIP and less than stellar K-to-BB ratio, Feller managed to accumulate 66 Wins-Above-Replacement (WAR) over his career, good for 31st all time.

Feller’s accomplishments, however, cannot be summed up by any statistical analysis of his performance on the diamond (traditional, sabermetric, or otherwise).  Feller was an innovator, a game-changer in the business of baseball.

When Feller returned from the war, he focused on the business of baseball.  Long before players signed multi-million dollar endorsement deals and benefited from the open bidding for their talents via free agency, Feller understood that the way to significantly increase earnings as a ballplayer was to cultivate and promote his personal brand.  The pitcher incorporated himself, created and participated in barnstorming tours in the off-season, and endorsed various products.  His barnstorming tours typically included Negro League players, helping to alter the perception of black players in a favorable way leading up to integration.  Additionally, Feller used what little leverage he had as a player in those days to negotiate various clauses in his contracts that paid him a bonus based on fan attendance when he took the mound.  His focus on the business of baseball also led him to become the first president of the Major League Baseball Players Association.  (NPR has a great summary of Feller’s off-field accomplishments and innovations).

Feller was, at base, one of the greatest fans and ambassadors of the game besides being one of its greatest players.  In a great piece this morning about Feller, Joe Posnanski of SI.com relates a conversation he had with Feller in which the latter neatly summarizes the romanticism and broader purpose of the game:

And then Bob Feller asked me about my father. Direct questions. Did he play catch with me when I was young? I said yes. Did he take me to baseball games? I said yes. Did he believe in me deeply? I said yes.

The tape recorder was off and my notebook was put away and so I cannot write here what he said word for word. But I remember the important part. He told me that I was lucky, that what you need to succeed in this world is a father who believes in you. And he told me that his father believed in him. Funny thing, though, he said Bill Feller never once said, “Bob, someday you’re going to pitch in the big leagues.” No, there were no words. There are some things that cannot be said with words. There was only those sweaty Iowa afternoons and those chilly Iowa evenings, and the sun setting, and a baseball going back and forth. Everything he needed to know about life was in that back-and-forth.

Bill Feller died in 1943, while his son Bob was at war. He had seen his son become the best pitcher in baseball.

As a parent I cannot think of a better way to capture what is special about sports, and baseball in particular.  It allows us to connect with our children in so many ways.  It allows us to encourage them, to teach them lessons that apply far beyond the little league diamond, and to create bonds and memories that will last long after we are gone.

Rest in peace, Bob.

Schwarz traces the history of baseball’s obsession with statistics to Henry Chadwick, a journalist and baseball writer widely acknowledged as the grandfather of baseball statistics.  Chadwick’s work in the mid- to late-19th century laid the foundation for much of the statistical framework through which we appreciate the game today.  Chadwick was adamant that the new game of baseball required a fair accounting of player performance:

In order to obtain an accurate estimate of a player’s skill, an analysis, both of his play at bat and in the field, should be made, inclusive of the way in which he was put out; and that this may be done, it is requisite that all…contests should be recorded in a uniform manner.

Anyone who has paid even scant attention to the debate regarding traditional and sabermetric approaches to the game of baseball will recognize Chadwick’s logic–not only do position players contribute to their team’s success by creating runs through their offense; they can also prevent runs by the other team through their defense.  Traditionally, players came to be valued and compensated based largely on their offensive production.  When it came time to arbitrate salaries or negotiate free agent contracts, offensive statistics carried the most weight.  (Whether or not the offensive statistics being used were the most accurate is a much larger debate, and Schwarz gives ample space to this history as well).

As the analysis of baseball became more sophisticated, analysts were finally able to measure a player’s total value by incorporating runs produced through offense as well as those saved by defense.  Rather than relying on the traditional fielding percentage (which simply measured the number of chances a fielder converted to an out), more sophisticated measures allowed for talent evaluators to look at how many runs a player saved in the field. Power hitting shortstops surely contributed to their team’s success by creating runs, but their light hitting counterparts could conceivable contribute just as much by saving runs.

Chadwick’s quote highlights two critical issues for any organization, and it is a theme that runs through The Numbers Game; ensuring that the metrics you rely on account for all of the ways a person contributes to success and that the data used to calculate those metrics is collected in a consistent, uniform manner. For years, teams overpaid for free agents whose offensive statistics were not viewed in the context of their defensive statistics. In some cases, a player’s offensive production was either tempered or cancelled out by their poor defensive play–in short, they were giving up as many runs with their poor defense as they were earning through their offense. While many organizations today have embraced the “analytics revolution” one wonders to what extent they truly know what metrics matter.  Like traditional owners and managers in baseball, it is likely that many businesses assume they know what the proper metrics are–what behaviors contribute the most to success.  The lesson from baseball is that accepted wisdom, personal experience, and “gut feelings” may be right, but they must be subjected to rigorous analysis to determine the best way to evaluate performance and drive success.

The Numbers Game is full of examples of how certain metrics came to be the accepted wisdom and the difficulty of any endeavor to unseat them in favor of better measures.  Besides baseball fans, anyone who is leading a business analytics initiative or responsible for performance and compensation will find this book rewarding.

If someone like Hernandez wins this year it would truly represent a paradigm shift in the way baseball writers evaluate player performance.  In the history of the AL Cy Award, no starting pitcher has ever won with less than 16 victories (Zach Greinke won last year).  In the NL, only Fernando Valenzuela managed to win the award with as few as 13 wins, and that was in 1981, and no winner from either league had a record as close to .500 as Hernandez does.

That being said, I would actually argue that Hernandez is not the only “non-orthodox” contender.

There is only so much control a pitcher has over the outcome of a game.  And while starting pitchers have more control than most, they still must rely on their defense to play well and on their offense to score runs.  So rather than focus on statistics such as wins (which are heavily dependent on a team’s offense), we should evaluate starting pitchers on their performance independent of their offense and–to the extent possible–their defense. Doing this means focusing on how often hitters deny batters the chance to put the ball in play (strikeouts), how often they give a batter a free pass (walks), how many base runners they allow (WHIP), and how deep into a game they pitch, which gives their bullpen rest and allows their manager to use only the team’s best relievers (thereby, giving the team the best chance to win).

So let’s look at a few statistics:

K/9 – Strikeouts per 9 innings: The more batters a pitcher strikes out, the better.
K/BB – Strikeouts to Walk Ratio: The more strikeouts relative to walks, the better.
WHIP – Walks + Hits per Inning Pitched: The fewer baserunners a pitcher allows to reach base, the better.
FIP – Fielding Independent Pitching: Measures a pitchers performance independent of the quality of their defense.  Lower the better.
RS/9 – Run Support per 9 innings: How many runs a pitcher’s offense scores for them per nine innings.
IP/GS – Innings Pitched per Game Started: The more innings pitched per start, the better.

I’ve created a table with non-counting statistics for the top 10 pitchers in the AL this year, but I have not included their names or their traditional statistics (Wins, ERA, or K’s).  Take a look and think about who jumps out as the best pitcher:

Now, all of these guys are good, but there is one whose performance really jumps out.

First, it’s hard to miss the obvious gap between Pitcher A and their K/BB ration of 10.28 and the rest of the field.  For every 1 batter Pitcher A walks he also strikes out 10.  That is more than double the next closest pitcher (Pitcher B at 4.31).  That ratio of 10.28 is the second highest in the history of baseball and only the third time we’ve seen a double-digit ratio (the other other two times-1994 and 1884).  Pitcher A also had the lowest WHIP, the lowest FIP, and the highest IP/GS.  The only two areas he didn’t finish first is K/9 (10th) and RS/9 (4th fewest).

So who is Pitcher A?  Felix Hernandez?  Nope.  It’s Cliff Lee.

Here’s the chart with the names included:

In terms of the traditional statistics, Lee only went 12-9 with a 3.18 ERA (6th in the AL) and 185 strikeouts (10th in the AL) in 28 starts.  At first blush, his body of work doesn’t look that impressive.  But if you go beyond mere “counting” stats, Lee’s dominance becomes more evident and Hernandez-esque.  His higher ERA (still 6th best) can be explained by an unusually high .302 batting aver for balls in play (BABIP), meaning when batters actually managed to put the ball in play they reached based 1/3 of the time.  BABIP is strongly correlated to ERA.  My guess is that Lee’s high BABIP can be explained by the fact that the defense behind him wasn’t the greatest, reflected in the fact that he had the best fielding independent pitching in the AL amongst starters.

Hernandez had less run support (3.10 to 4.45) and more strikeouts per nine innings (8.36 to 7.84), but otherwise Lee was better than Hernandez in every non-counting category (and he was better than every other contender).

Will Lee win the AL Cy Young?  I doubt it.  My guess is it will either go to Hernandez or CC Sabathia (since he had 21 wins and played for the Yankees in the AL East), but it is hard to argue with how dominant he was over the course of the regular season.

[C]ompetitive balance improved from the 1930s until leveling off in the late-1980s and early-1990s. Much of this improvement was likely a natural consequence of more high-quality talent becoming available to more clubs, the addition of the amateur draft in 1965 (the mechanism Branch Rickey felt was most important for leveling the financial playing field across teams), and other minor structural tweaks to the league. Why would the improving trend disappear just as revenue sharing came into existence? While I’m not certain that revenue sharing stopped the progress, I doubt it was instituted just in time to counteract a trend reversal.

In my view, if revenue sharing worked, there would be some evidence of it working over the past two decades that it’s been tried under various formats. How much longer are we supposed to give it, especially when what we observe is exactly what theory predicts we should observe? If we think it’s important to correct inherent differences in revenue potential across teams, I think revenue sharing is a poor tool for achieving that goal.

For MLB teams, Bradbury shows that revenue earned on wins is pretty stable between 60 and 80 wins (approx. $100M).  80 wins represents an inflection point at which revenue in relation to wins climbs rapidly–e.g. teams that win 100 games can earn 80% more than teams winning only 85 games.  The problem is that many teams can spend relatively little and turn a solid profit by fielding a team that finishes the season 20 games under .500.  Rather than poor the shared revenue into their teams, owners have found it easier and safer to field a sub-.500 team.

It strikes me that MLB needs to think harder about the choice architecture it puts in place with revenue sharing. If teams can make a profit without being competitive and the returns on winning do not kick in until you surpass the 80-win mark (and, therefore, incur greater risk and less guaranteed profits) why not break up the disbursement of revenue sharing funds?

For example, why not disburse the funds in tranches? You could make additional tranches dependent on hitting certain win milestones and with a higher number of wins comes greater fund sharing (e.g. as teams approach 80 wins they are provided with an additional kicker that can be put toward the following season). It may also make sense to make the revenue shared less than the profits obtained at >80 wins. This way, teams are provided with an incentive to take greater risks and build more competitive teams and not fall back on the shared revenue.

Once you can incentivize a team to make an investment in talent above and beyond what they currently provide the team may take on a life of its own, creating a positive feedback loop that could catapult it into the 80+ wins area and thereby generate greater profits which allows for greater investment which provides greater talent, etc, etc.

Just a thought.

It was not surprising that Greinke won, since his earned run average, 2.16, was the lowest in the American League since 2000. But his decisive margin of victory over Seattle’s Felix Hernandez was a sign that voters overlooked his deficiency in another bedrock statistic: wins.

Greinke tied for seventh in the league in victories, with 16. The lowest previous total for an A.L. starter in a nonstrike season was 18.

To what extent voters are finally starting to incorporate the perspective of sabermetric analysts (e.g. many traditional statistics, like wins, distort the quality of individual performances) is still unclear.  However the vote for Greinke is pretty good evidence that progress is being made.

What’s more exciting for me is that not only was a pitcher voted the award who was dominant in newer, alternative statistical categories, but that he himself incorporates these statistics into his game plan and pitching strategy:

Bannister [Greinke's sabermetric-friendly teammate] said Greinke has learned to adjust his pitching based on the advanced defensive statistics. Because of the size of the outfield at Kauffman Stadium and the strength of the Royals’ outfielders, relative to their infielders, it sometimes made more sense to induce fly balls.

“David DeJesus had our best zone rating,” Bannister said, referring to the Royals’ left fielder. “So a lot of times, Zack would pitch for a fly ball at our park instead of a ground ball, just because the zone rating was better in our outfield and it was a big park.”

To that end, Bannister introduced Greinke to FIP, or Fielding Independent Pitching, the statistic Greinke named Tuesday as his favorite. It is a formula that measures how well a pitcher performed, regardless of his fielders.

Music to my ears.

Now, there is an entirely separate debate whether Beane and his approach actually work.  Instead, the article focuses on whether publicizing Beane’s approach had a significant effect on the way GM’s built their teams.  The basic hypothesis is that teams should place greater value on hitters with higher On-Base-Percentages than other, traditional offensive statistics, such as Home Runs (HR) and Runs-Batted-In (RBI). If the correlation between player compensation and OBP increased while correlations with traditional benchmark statistics decreased that would point to the potential influence of Lewis’ book on the game.

So what actually happened?

The year 1997 may be an outlier in this case, but the correlation between the two [salary and OBP] was .31 in that year. We see that in the late ’90s and early oh-ohs, the correlation danced between .40 and .50. In 2001, one year prior to Moneyball, it was at .44. By 2004, it was .64. The strength of the correlation (as measured by R-squared) about doubled. Coincidence? Maybe. But maybe, just maybe, the people who actually make the decisions in baseball actually read and accepted the conclusions in Moneyball.

Note that in the years before Moneyball, HR and RBI clearly drive the market much more clearly than does OBP. By 2004, the jump in OBP’s popularity had pulled it even, partly because HR and RBI fell in their correlative power. In 2005, OBP was actually the better correlate of salary. Chicks may dig the long ball, but apparently nerds were running the front office of your favorite MLB team. Look what happens after 2005 though. There’s a general downward trend for all three stats. It’s likely that OBP did have its day in the sun, but why would HR and RBI, so long dominant, also fall?

It is an interesting analysis. My skepticism lies in the fact that while we see some interesting correlations it is hard to parse causation out of this. It is clear that the market began to correct itself and alter how it values certain skills. But was this the direct result of Lewis’ book? Or was Lewis simply providing an anecdote representative of a larger, growing trend around baseball? There does seem to be a general trend upwards (if you smooth out the yearly volatility) in terms of salary and OBP even prior to 2002. (Of course, I would be curious to see the data set–are there a few powerful outliers driving the results? Were there a few, big market teams overspending on these types of players?) Also, a number of Beane acolytes went on to run their own ball clubs while the Red Sox hired a young, Moneyball-minded GM names Theo Epstein in 2002 as well as sabermetric godfather Bill James in 2003 (the two would build the team that would eventually win the World Series in 2004 and again in 2007). Likely it was a bit of both–an already growing trend within the game given additional validation and exposure via Lewis’ brilliant book.

What jumped out at me was the number of times Rivera pitched in more than 2 innings*: 52 out of 88. In this day and age where closing has become even more specialized and pitchers in this role rarely throw in more than one inning, Rivera took the rubber in multiple innings almost 60% of the time.  This included a three inning, nine out performance against the Boston Red Sox in game 7 of the 2003 ALCS (the famous Grady Little game).

*By innings I mean separate half innings–innings here does not mean that Rivera needed to get 3 outs each time he took the mound.

(Via Nate at FlowingData)

Well, it is performance review time at work and this reminded me of a post I’ve been meaning to write for a while.

An issue that has always interested me is how organizations measure individual performance.  Organizations have finite resources and therefore must deploy those resources in the most efficient manner, maximizing their value.  Given the large percentage of resources invested in personnel, organizations have a huge incentive to get those investments right.  However, calculating an accurate ROI on employees is probably one of the hardest things to do.  To explore why–and how it might be done better–I turn to the world of sports, baseball in particular.

There has long been a debate within the Sabermetric community (and between purists and Sabermetricians) regarding the statistical relevance of “clutch”: the ability of a player to elevate their performance in key situations in a manner significantly different from their performance in normal situations.  Early research by such pioneers as Bill James found that the attribute of clutch didn’t exist–much like the idea of a “hot-hand” in basketball, the appearance of a clutch performance (e.g. a usually mediocre batter managing to hit .500 in a playoff series) was nothing more than a statistical artifact.  If you were to look at any 5-7 game stretch during the 162-game regular season you are just as likely to find even average hitters going 4 for 8 or 8 for 16 as you would be to find them going 2-8 or 0-16.  Over a long enough time period, these streaks even out and players regress to their mean performance.  Basically, if you are a .250 hitter, over a long enough period of time your performance will return to its mean, despite occasional swings to the extreme left and right sides of the bell curve.

More recent studies have looked to expand upon earlier research and refine how we search for clutch performances.  A common way to do this is to not look at single games that were more important (e.g. post-season play), but rather particular moments that alter the probability of a team winning that particular game.  This approach has been termed “leverage“:

the swing in the possible change in win probability. If there is a game with one team leading by ten runs, the possible changes in win probability, whether the event is a home run or a double play, will be very close to negligible. That is, there won’t be much swing in any direction.

But, in a late and close game, the change in win probability among the various events will have rather wild swings. With a runner on first, two outs, down by one, and in the bottom of the ninth, the game can hinge on one swing of the bat—a home run and an out will both end the game, but with vastly different outcomes for the teams involved.

From left to right: A-Rod and Jeter

An excellent example of this debate over which players are clutch and which are not is the case of Alex Rodriguez. Long touted as one of the greatest hitters of all time, A-Rod has consistently been criticized as a non-clutch performer, accused of accumulating the bulk of his stats in low-leverage (i.e. low-pressure) situations. One could not listen to NY sports radio over the past 5 years without hearing fans decry the ‘superficial’ performance of the Yankee slugger while praising the clutchness of his statistically less impressive teammate, Derek Jeter. Part of the reason for this perception is the effect of high-impact events on perception and the framing of the debate by the media. But when we look at both players’ statistics in leveraged situations a much different picture emerges:

[As of 2008] Mr. Rodriguez has hit for the clutch throughout his remarkable, surefire Hall of Fame career. His career OPS in high-leverage situations is .975. In medium-leverage, it’s .960. And in low-leverage, it’s .972. That’s consistent with the American League as a whole during his career, when each year batters in high-leverage situations hit somewhere between 1% worse and 6% better than they did in low-leverage situations.

Additionally, if you look at the playoffs (where most look for clutch performance), A-Rod has accumulated the following statistics (including this year): .299BA/.393OBP/.958OPS. Here’s Jeter’s: .309BA/.379OBP/.858OPS. In series where his team is playing for a spot in the World Series, A-Rod’s stats are even better–Jeter’s go south.

The point is not to cheerlead for Alex Rodriquez, but rather to point out two points that can be applied to any organization and field when thinking about the value of personnel:

1. When evaluating performance we should pay attention to results over the long term, not an arbitrary chunk of time. Anyone can ‘get hot’, but that doesn’t mean the individual willed themselves to a better performance or that current success is a predictor of future success. Discrete outcomes are quite dependent on factors outside of an individual’s control as well as the randomness of performance. This works both ways–for successes and for shortcomings.
2. Single, high-impact events can significantly skew our perspective. Objectively evaluating the quality of individuals is difficult since we tend to use anecdotes as mental shortcuts. Big wins are important, but they shouldn’t blind us to the overall performance of an individual. Those big moments could easily be the result of random chance rather than talent. Now, we all benefit from luck from time to time. However, much time and treasure has been wasted on individuals who ‘came up big’ at one time and management is just waiting for them to rise to the occasion yet again. This applies equally to ‘hot’ baseball prospects, CEO’s, coaches, sales personnel, programmers, managers, etc.

So what should we do? How do we better measure human capital performance? I think partly we should take a page from baseball. New measurements have emerged over time to better evaluate player performance. Taking an analytical approach to measuring performance is step one. It shouldn’t be the only measurement, but without appropriate data organizations are simply basing their evaluations on subjectivity and intuition–you need a balance. With new measurements comes a new mindset; the prism through which we view the world changes so that instead of looking for those ‘big moments’ we now look for consistent performance over the long term. A healthy appreciation for randomness, chance, and methods for accurately measuring performance are all good first steps towards more accurately evaluating human capital.

For those that made it this far, thanks for sticking with me on this one.  Would love to hear your thoughts.