Who is the most skilled team in the League?
Merriam-Webster defines skill as the ability to use one’s knowledge effectively and readily in execution or performance. In baseball, or any practical judgment of one's skill, it is not as easily defined. What may be skill, could actually be luck.
For instance, in finance, a portfolio manager could be lucky by selecting the right stock in one quarter, producing outsized alpha relative to a benchmark. But then, the next quarter, that stock does poorly. This could mean prior returns are more likely a result of “good luck”, rather than “good skill” that can persist for multiple periods. Skill persistency is more important in this instance.
The assessment of skill in sport is no different. In baseball, our topic today, the perception of skill can most assuredly be masked by luck in any given season. Either through a higher than league, or historical player, average batting average on balls in play (BABIP), or through a higher-than-normal strand rate for a pitcher— among other reasons.
In some cases, the barometer of luck outweighs the skill, and a player is projected to repeat the same performance the following season. Yet, luck is a fickle friend and can show up sporadically, letting you down if you were desperately relying on it for another round of success.
There are, however, some signs of skills that create a more individualistic profile of a player. Metrics that seek to strip out implicit biases, or random chance. It doesn’t eliminate it, but it does mitigate it — creating a more defined view of a player skills that could potentially repeat season over season.
In this post, I will use an ensemble metric of a player skills to gauge a League members overall team skill. The latter will be created through applying a quantitative scoring system dependent upon a player's position, but also within versus the entire universe of players.
Assessing Skill
In order to provide an objective view at skills, I will leverage the Baseball HQ metric of Base Performance Value (BPV). This is a unified performance metric that allows for an equivalent comparison between hitters and pitchers. It is also an ensemble metric of skills, combining multiple metrics within it. None of which are the traditional counting stats than can be skewed by luck.
For hitters, the BPV takes a players walk rate, contact rate, power index, and a statistically scouted speed score. These sub-metrics are defined as below:
- Walk Rate: A measure of players patience as defined by BB/(AB-BB). The best players will have a rate above 10%
- Contact Rate: A measure of players ability to get wood on the ball defined by (AB-K)/AB. The best players will have a level above 80%.
- Power Index: A standardized measure of power that compares a players output (doubles, triples, and home runs) weighted by regression beta figures to the league average power metric. A level over 100 equates to above average power, with 150 being elite.
- Statistically Scouted Speed Score: A skills-based metric that does not rely on stolen bases. A figure above 100 equates to above average speed.
These metrics are weighed based on regression betas to create a total BPV for player. A level over 50 is above average.
For pitchers, a BPV is created by combining a players strikeout percentage (K/Batters Faced), walk percentage (BB/Batters Faced), and ground ball rate as a whole number. Each of these characteristics are no effected by external factors, and therefore can provide a more accurate view on a pitcher's skill. A lever over 50 for starters is above average, with a level greater than 75 as an indicator of above average skill for relievers.
The metrics used to create a team view of skill are the projected BPV for each player, as forecasted by Baseball HQ. To remove outliers, hitters with less than 250 projected plate appearances are removed. And for starters, players with less than 110 innings projected are remove, as are relievers with less than 40 innings. Given these screens, a team with many prospects are penalized within this assessment of skill, as those players receive a zero final score. There are also some deep keepers that do not have any projections at all. Yet, 93% of rostered players are accounted for, providing a strong population for analysis.
To normalize these scores and create a final score to use in evaluating the overall team, a z-Score ((value — average)/standard deviation) metric was calculated for the entire hitter and pitcher universes, independently. Similarly, a z-Score was created based on player positions, creating a metric to ascertain which team has the more skilled first baseman or second baseman for example. Given that some players have multiple position eligibility, only a primary position was accounted for.
For the final team score, the z-Scores are added together as a method to show total combined skill. For the individual positional skill assessment, the positional z-Score for a given team is averaged. The difference in approach is meant to mitigate the skewed outcome of a team having multiple players at one position.
For example, if one team has four first basemen and each have a score of 0.2 that should not be perceived as having the most skilled position if another team has one first baseman scored 0.8-given that you can more likely than not only start one first baseman at a time. This is the old age conundrum of having to fight a bear size duck or 100 duck sized bears. There is no “correct” answer, but this approach seeks to normalize any skewed results.
The Results of a Quantitative Skilled Base Review of the League
With the above as prologue, the below are the results. And before an Owner takes offense at the results, feedback and criticism is welcome. Additionally, this is a view based on a point-in time, and perhaps the path to victory does not rely on player specific skills — but rather the skill of the owner to navigate a lengthy season and make the tough choices.
As shown below, a team's skill is measured in four parts: overall, overall ex-relievers, hitters, and starters.
In two out of the four screens, the same team came out on top: Ben Rosenfeld and Expansion Mansion. One screen this team does not register a top score with is hitters, a signal that their main strength comes from pitching alone — an indication of a lack of balance.
Yet, the one team with the best overall team score is Ryan Amari and the FC Union Somerville. However, this team's strength comes from their closers, and that is a one-dimensional category that is unlikely to produce a high strikeout and win counting totals — leading to low roto figures in those categories.
Given the above dynamic, a total z-Score Ex-Closers is more indicative of team skill, and here Expansion Mansion does lead.
Based on the total z-Score Ex-Closer metric, the other top teams are Jon Hines, Matt Belair, and myself. Interestingly, the champions from the prior four seasons are ranked in the bottom half of the league, and this is with Shoei Othani being counted twice for last year's champion, the Saucy Tortugas and Kevin Flight. Their team weakness mainly stems from poor hitter scores, and this is more apparent in the positional view below.
The view above also illustrates the non-normal distribution of catchers, as the average z-Score for most is above 2 — indicating the average score for a team is 2 standard deviations above the mean. This is a result of some Catchers having a negative BPV value. For catchers, the distribution is said to be right tailed.
With this positional view, it also illustrates Jon Hines strengths. He leads the league at 2B, SS, and is second at OF. But it also shows Ben Rosenfeld’s team is really concentrated on pitching strength and could struggle on offense. The mirror opposite of Hines and could lead to deal making between these two Hopkington based teams.
The chart below helps further showcase the differing strengths and weakness for each team at the positional level.
Let’s have a good season!
A lot can happen to between now and September, and these skills-based metrics are not 100% perfect. After all, you still have to play the game — and part of the game is about having the right talent. Yet, hustle beats talent when talent doesn’t hustle, and winning a championship in this League is making sure you are always hustlin’ and making the right intra-season moves.
Good luck to all and I hope you enjoyed this analysis as much as I did puting it together.
For those that want to dig into this analysis, my data sheet can be found here below:
