Ranking tennis players: Method (part 2)

Having previously decided on service games won and lost with no surface adjustments in assessing an individual tennis match, it remains now to take the step from judging a single match to combining the results of over 7000 matches (an approximate total for a full ATP World Tour, ATP Challenger Tour, and Davis Cup World Group season) into an overarching rating.

Ranking tennis players: Method (part 1)

My last post concluded with the statement that I would be presenting a tennis ranking system based on the answers to two questions: How did you play, and who did you play? Before building such a system, however, it is first necessary to decide on the meanings of the questions themselves. I’ll tackle the queries in order.

Ranking tennis players: Motivation

When introducing a statistical system in sports, it’s always worth considering what purpose the system will serve. In this case, since the system is a ranking of tennis players, assessing the rankings used by the ATP, the main officiators of men’s tennis, is a natural starting point in determining whether a new system intended for the same purpose can be of use.

The top 5 postseason relievers ever

To complete the set of postseason lists (or at least the positive ones), here are the top 5 relievers of all time by Championship Probability Added. It’s a top-5 list rather than a top-10 because relievers haven’t been a major postseason factor for nearly as long as starters or position players, and because it’s harder for most of them to accrue as much positive value. (Negative value is much easier, but that’s for another post at another time.)

The top 10 postseason starting pitchers ever

Having gone through the best hitters in playoff history, the natural course of action is to switch over to the side of the game that tends to dominate October – the pitchers. So here are the 10 best starting pitchers in playoff history, in reverse order.

Lance Berkman: Stealth October Superstar

As promised, here is the breakdown of the overpowering, overlooked postseason career of the third-ranked hitter in Championship Probability Added:

The 20 best postseason hitters ever (by one method, anyway)

Since the postseason evaluation of this year's Hall of Fame ballot turned out to be so pitching-heavy, here’s something to counterbalance it: the top 20 postseason hitters of all time, by Championship Probability Added, with accompanying breakdowns of their performances.

Postseason performance on the 2014 Hall of Fame ballot

Now that we have a framework in place to evaluate postseason performance, we can start applying it to specific questions. And since it’s that time of year, let’s start with the Hall of Fame ballot.

For discussion, reference, and whatever other application you prefer, here is the postseason performance of the players on the 2014 BBWAA Hall of Fame ballot, by Championship Probability Added. We’ll do hitters first.

Evaluating postseason performance: Championship Probability Added

Who is the best hitter in baseball history?

It’s a common question, and one with a number of available responses. You can pick the player with the highest batting average (Ty Cobb), on-base percentage (Ted Williams), or slugging percentage (Babe Ruth). If you prefer counting stats, you can take the leader in hits (Pete Rose), runs (Rickey Henderson), RBI or total bases (Hank Aaron), or home runs (Barry Bonds). Ruth would probably be the most common statistical answer, but the basic (and advanced) statistics are the beginning of the discussion, not the end. There are numerous other arguments you can make – you can compensate for park effects or military service or league strength and come up with Williams or Bonds easily enough.

Who is the best postseason hitter in baseball history?

Or, more to the point, how do you answer that question?

Introduction

Near the end of the 2012 baseball season, a rather spirited debate sprung up around the selection of the American League MVP. The more traditional baseball minds rallied around Miguel Cabrera and his Triple Crown season, while the statistical analysts backed Mike Trout and his almost-as-good hitting that was accompanied by significantly better defense and baserunning.

This debate was handled rather exhaustively at the time, and was even re-done a year later, albeit less emphatically. My intent is not to revisit the arguments here. I reference them only as context for a quote from Minnesota Twins manager Ron Gardenhire (taken from this article) that I thoroughly enjoyed at the time:

"All I want to tell you is if you're going to for a Triple Crown, and you've got (Cabrera's) numbers, you can SABER all you want to -- those numbers blow your mind."

The quote is notable mostly for the simultaneous misuse and misspelling of SABR, the abbreviation for the Society for American Baseball Research (to be fair to Gardenhire, it was a spoken quote, so the misspelling is likely the writer’s rather than his). Goofiness aside, the quote also provides the theme for the work that will appear in this space.

This will be a blog dedicated to the fusion of sports and mathematics, topics I appreciate independently but particularly enjoy when combined. The primary, though not only, sports that will be addressed will be baseball and men’s tennis. There is, of course, already an enormous amount of baseball analysis available online. I don’t particularly intend to compete with the ongoing cutting-edge research in fielding or Pitch-FX or pitch framing, or to develop yet another WAR system. If there’s any groundbreaking baseball research done here, it will be less because I’ve come up with any brilliant insights than because the topic isn’t weighty enough to command the attention of a real analyst.

On the tennis side, most of what you see will probably be a bit more like what you’d expect from an Internet analyst, if only because there’s less freely-available work of that type associated with tennis (or at least less that I’m aware of). But even in coming up with my own player rankings, I don’t intend to present them as flawless, ironclad, or anything other than (hopefully) interesting. I try to maintain my own awareness of the limitations of this type of work, and will thus strive for a tone more conciliatory than that of the stereotypical strident stathead.

With regard to the math itself, the complexity will vary from addition up to the occasional bit of statistical modeling. I will make an effort to explain the semi-complicated stuff as much as possible and warn in advance when it shows up. And on the topic of advance warnings: People who do this sort of analysis have sometimes been branded with the reputation of trying to ruin other people’s enjoyment of sports. This is the diametric opposite of my intention. I’m looking to enhance the sports experience here, not detract from it; if reading my writing does not serve that purpose for you, then I would encourage you to look elsewhere for work that will do so.

If a collection of writing about mathematical analysis of sports appeals to you, however, then here’s hoping you’ll find this a useful and entertaining source for just that sort of material. With Ron Gardenhire’s stated permission, I hereby welcome you to SABER All I Want To.