Role of the analytical staff
The role of analytical staff is to communicate relevant information to a diverse group of end users in a format that is actionable and understandable for them to improve task performance and operational efficiency. One of the more successful franchises, the Houston Astros under Jeff Luhnow, refers to their analytics department as “decision sciences”, which aptly describes the roles. They acquire information and develop models that help their decision makers - the GM, farm director, scouting director or manager – make better decisions.
Skills that are helpful to your career:
- analytical and creative mind
- advanced excel skills
- reporting and data visualization skills
- scripting and statistical language skills
- SQL programming skills
- teamwork
- skills in any of the following are helpful (R, SQL, Python, Tableau, SPSS)
- skills with Big Data tools (Hadoop, Hive, Pig)
In some manner, shape or form analytics now permeates every decision a team makes from hot dog prices to contract negotiations. The top role would be to put a winning product on the field given whatever budgetary constraints are mandated by either team ownership or the Collective Bargaining Agreement (CBA). Given those parameters, roster construction and control of player assets would seem to be at the top of the list, from which success in other areas would follow.
As illustrated by the website https://www.thebaseballgauge.com/ there are three main ways to acquire and develop talent:
Internally/Home-Grown:
- Through the Rule 4 Draft (conducted in June)
- International Free–Agents (International signings) or externally
- Via Trades, Free–Agent signings, Rule 5 draftees
https://www.thebaseballgauge.com/history.php?first=min&last=max&tab=tm_analysis (slow to load)
Home-Grown as they define the term appears to include the sum of Rule 4, and Amateur Free Agents (INTL), with some rounding issues. Approximately 40% of productivity from talent (WAR) comes via the Home-Grown route. Trades provide 38% and free-agent signings 22%. Trades seem to be where the highest % of WAR is accumulated according to these numbers and they also appear to more highly correlated to winning percentage of late. Evaluating trades is becoming a more important task for analysts and becomes more difficult if prospects are involved as opposed to players with a MLB track record.
I compared the cumulative WAR for each roster approach for the most recent season ended, 2018 and from seasons 1991-2013 to see if there was change over time in the effectiveness of each approach.
Correlation of WAR to Win %
| Approach | 2018 | 1991-2013 |
| Home Grown | 0.56 | 0.56 |
| Trades | 0.80 | 0.48 |
| Draft | 0.50 | 0.34 |
| Free-Agents | 0.46 | 0.37 |
| Amateur Free-Agents | 0.20 | 0.49 |
| Other | 0.05 | -0.41 |
Analytics is very important in deciding on value for arbitration cases. Players in the 1-6 year service time bucket are getting paid higher and higher amounts based on prior decisions and reasonable comps. Front offices can save salary dollars by making prudent judgments and avoiding arbitration entirely or making reasonable offers so that they win when they are forced go to arbitration. A framework for financial resources needed could be made based on this and other pieces of information and would vary based on where each franchise sits currently and how they have executed in each area recently. Given that, approximately 40% of productivity from talent (WAR) comes via the Home-Grown route. Trades provide 38% and free-agent signings 22%. Trades seem to be where the highest % of WAR is accumulated according to these numbers and they seem to be correlated higher to winning percentage more recently. Evaluating trades is more important and more difficult if prospects are involved.
Rosters and priorities can change in a hurry given players aging curves, which have been shifting to the left since the end of the “PED era” and the advent of more stringent drug testing that includes amphetamines.
Role of analytics for “On the Field” in-game decisions
Some of the most exciting developments in analytics are in this area due to technologies like TrackMan, Rapsodo and Flightscope among others. It allows fans in many respects to see, and analysts to quantify objectively instead of subjectively, why players are great.
Lineup optimization, which "The Book" by Tom Tango and others goes into has caused us to re-think where to place hitters in the lineup to optimize teams run-scoring ability. Player usage is changing before our eyes with the use of defensive shifts and “openers” taking the place of starting pitchers. Credit is given to the Rays for accelerating the use of both, but in fairness, the concept of “openers” was discussed in the classic sabermetrics book “Percentage Baseball”, by Earnshaw Cook published in 1966.
Cook laboriously and meticulously proposed that pitching staffs, who were previously going 5 innings by the starters, to be replaced by a reliever going 2 innings then another closing the final two innings (S5: R2: R2) would be better utilized and workload more efficiently allocated by a system where a reliever would go the first two 2 innings (he didn't call it an opener), then the “starter" would go 5 innings, then a closer would pitch the final two innings. (R2: S5: R2). Cook added that each pitching change would be initiated by a pinch-hitter, which would improve offensive production at the same time pitching assets would be used more productively. This was pre-DH and considered revolutionary, so it was mocked and ridiculed. The Rays may have proved that although necessity may not always be the mother of invention, it certainly can be a catalyst for action.
Wearable technology shows the promise of allowing training and medical staffs to manage workload and usage better and achieve, if not the holy grail of predicting or preventing injuries in their entirety, at least reducing them significantly.
Quantifying intangibles is an area that warms my old-school heart and “In Search of David Ross” or Jonny Gomes is now at the top of my reading list. I know “not everything that can be counted counts and not everything that counts can be counted” but you still must make the effort. Intangibles – Luck – Residuals what’s the distinction?
I’ll close with a Bill James' quote/question: “What is the relevance of sabermetric knowledge to the decision-making process of a team?” Answering that question, to me, is the role, the purpose and the mission statement for data analysts. The next article will be about the use of analytics in arbitration, salary negotiation and valuation concepts.
Charles Slavik is a Sport Management student at University of North Florida, Go Ospreys!! and is primarily interested in data analytics and baseball. He can be reached at https://twitter.com/theslav1959 or read at The Slav's Baseball Blog - BASEBALL 24-7-365 http://slavieboy.blogspot.com/ .
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