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.
Wednesday, January 29, 2014
Sunday, January 26, 2014
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.
Thursday, January 23, 2014
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.
Monday, January 20, 2014
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.)
Thursday, January 16, 2014
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.
Wednesday, January 8, 2014
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:
Tuesday, January 7, 2014
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.
Sunday, January 5, 2014
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?
Saturday, January 4, 2014
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.
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