## Wednesday, December 04, 2019

In the past I’ve wasted time writing in a structured format, instead of just explaining how the metrics are calculated and noting anything that stands out to me. I’m opting for the latter approach this year, both in this piece and in other “end of season” statistics summaries.

I’ve always been interested in leadoff hitter performance, despite not being particularly not claiming that it held any particular significance beyond the obvious. The linked spreadsheet includes a number of metrics, and there are three very important caveats:

1. The data from Baseball-Reference and includes the performance of anyone who hit in the leadoff spot during a game. I’ve included the names and number of games starting at leadoff for all players with twenty or more starts.

2. Many of the metrics shown are descriptive, not quality metrics

3. None of this is park-adjusted

The metrics shown in the spreadsheet are:

* Runs Scored per 25.5 outs = R*25.5/(AB – H + CS)

Runs scored are obviously influenced heavily by the team, but it’s a natural starting point when looking at leadoff hitters.

* On Base Average (OBA) = (H + W + HB)/(AB + W + HB)

If you need this explained, you’re reading the wrong blog.

* Runners On Base Average (ROBA) = (H + W + HB – HR – CS)/(AB + W + HB)

This is not a quality metric, but it is useful when thinking about the run scoring process as it’s essentially a rate for the Base Runs “A” component, depending on how you choose to handle CS in your BsR variation. It is the rate at which a hitter is on base for a teammate to advance.

* “Literal” On Base Average (LOBA) = (H + W + HB – HR – CS)/(AB + W + HB – HR)

This is a metric I’ve made up for this series that I don’t actually consider of any value; it is the same as ROBA except it doesn’t “penalize” homers by counting them in the denominator. I threw scare quotes around “penalize” because I don’t think ROBA penalizes homers; rather it recognizes that homers do not result in runners on base. It’s only a “penalty” if you misuse the metric.

* R/RBI Ratio (R/BI) = R/RBI

A very crude way of measuring the shape of a hitter’s performance, with much contextual bias.

* Run Element Ratio (RER) = (W + SB)/(TB – H)

This is an old Bill James shape metric which is a ratio between events that tend to be more valuable at the start of an inning to events that tend to be more valuable at the end of an inning. As such, leadoff hitters historically have tended to have high RERs, although recently they have just barely exceeded the league average as is the case here. Leadoff hitters were also just below the league average in Isolated Power (.180 to .183) and HR/PA (.035 to .037)

* Net Stolen Bases (NSB) = SB – 2*CS

A crude way to weight SB and CS, not perfectly reflecting the run value difference between the two

* 2OPS = 2*OBA + SLG

This is a metric that David Smyth suggested for measuring leadoff hitters, just an OPS variant that uses a higher weight for OBA than would be suggested by maximizing correlation to runs scored (which would be around 1.8). Of course, 2OPS is still closer to ideal than the widely-used OPS, albeit with the opposite bias.

* Runs Created per Game – see End of Season Statistics post for calculation

This is the basic measure I would use to evaluate a hitter’s rate performance.

* Leadoff Efficiency – This is a theoretical measure of linear weights runs above average per 756 PA, assuming that every plate appearance occurred in the quintessential leadoff situation of no runners on, none out. 756 PA is the aveage PA/team for the leadoff spot this season. See this post for a full explanation of the formula; the 2019 out & CS coefficients are -.231 and -.598 respectively.

A couple things that jumped out at me:

* Only six teams had just one player with twenty or more starts as a leadoff man. Tampa Bay was one of those teams; Austin Meadows led off 53 times, while six other players lead off (this feels like it should be one word) between ten and twenty times.

* Chicago was devoid of quality leadoff performance in either circuit, but the Cubs OBA woes really stand out; at .296, they were fourteen points worse than the next-closest team, which amazingly enough was the champion of their divison. The opposite was true in Texas, where the two best teams in OBA reside.

See the link below for the spreadsheet; if you change the end of the URL from “HTML” to “XLSX”, you can download an Excel version: