Grand Slam Scores update: Mid-2016

As has been mentioned previously here (and in many, many other places), Novak Djokovic won the French Open. That completes the career Slam for him (and the consecutive Slam), making him the first player since a lot of different people to do a lot of different things. (Among others, he's the first since Rafael Nadal to complete the career Slam, the first since Jim Courier to win the Australian and French in the same year, and the first since Rod Laver to hold all four Slams at once.)

So let's check and see what Grand Slam Scores now think of his career as a whole. (And, you know, everyone else. But mostly Djokovic, come on.)

Grand Slam Scores update: Post-2015

And we're back (again). A second stolen computer and a move to a less theft-prone apartment later, and I should be up and running for the foreseeable future. On the other hand, the goal of posting regular ranking updates for the entire 2015 season has vanished just as thoroughly as my previous two laptops, so I'm taking the rest of this year to overhaul the Melog rating in a few ways. If my computer remains my computer, I'll plan to resume regular ranking updates at the beginning of next year.

For now, here's something a bit simpler: an update on where the best players of 2015 now stand in Slam Score.

Using Slam Scores: Difficulty of Final

Last time out, we used Slam Scores to evaluate which players might be overrated or underrated by conventional wisdom. This time, we’ll be… doing something almost identical, actually, but in a different way. It’s easy to find the players who’ve won the most Grand Slams, but it’s rare that anyone puts systematic consideration into the question of who was beaten in the process of winning them.

So let’s find out – which players have faced the toughest slates of opponents in Grand Slam finals?

Using Slam Scores: Overrated and Underrated

I've expended quite a bit of virtual ink in this space discussing Grand Slam Scores, which evaluate historical performance in Slams as a way of comparing tennis players. But any player evaluation method worth its salt should be able to do more than just supply an answer to "which guy is better?" questions. Are Grand Slam Scores up to that task?

Let's find out. We'll start by applying them to the question of which tennis players are the most likely to be overrated and underrated in conventional opinion.

Grand No-Slam Scores

We’ve spent a significant amount of time and virtual ink exploring Grand Slam Scores as a method for historical evaluation of tennis players – and so far, a significant portion of the findings that have resulted can be summed up by saying, “Roger Federer is really, really good.” While that is, of course, a perfectly valid conclusion to draw (and reinforce many times over), it is also not one that requires the development of new metrics to discover. So let’s expand our horizons beyond the Nadals, Samprases, Lendls, and Borgs of the tennis landscape, and use Slam Scores to evaluate some of the lesser lights of tennis history.

Grand Per-Slam Scores

We’ve spent some time now on Grand Slam Scores as a method for evaluating tennis players. We’ve used two different weighting methods, and broken the scores down Slam by Slam. But everything we’ve looked at so far has been a career total, and that is inevitably only part of the story.

So let’s move on to the other basic mode of evaluation: rate statistics. We’ll start with the top players in overall Grand Slam Score accumulated per Slam played:

Grand Slam-by-Slam Scores

We’ve already looked at two versions of Grand Slam Scores and seen how they treat the career totals of a variety of players. Now, it’s time to start breaking the scores down into their components. We’ll start out the obvious way: by looking at scores achieved in each individual Slam. As mentioned in previous posts on the subject, these breakdowns will be done through the lens of the odds-based scoring method, rather than the world rankings-based method.

Grand Slam Scores 2: The other weighting option

We’ve previously introduced Grand Slam scores as a simple-yet-effective method for evaluating players through tennis history through a lens that conventional wisdom can understand. The initial method used was to award 1 point for a title, and split that in half for each round further from the title that the player was eliminated. While that’s a mathematically satisfying way of handling things, there’s no reason to insist on it as the only possibility. And as it happens, there’s a readily-available alternative, one that’s already in use in a well-known venue.

Evaluating tennis history: Grand Slam Scores

One of the subjects I plan to explore regularly in this space is tennis history. It's a subject I've explored to some extent already, generally through the context of my Melog ratings. While I think the Melog system does a creditable job evaluating tennis performance, it is not without issues.

One of those issues is that the system does not match the evaluation of tennis players in popular opinion. I often find this to be an asset; Melog places value on excelling throughout the year, not merely in big tournaments. But it is inescapably true that most evaluators, including the players themselves, put far more emphasis on Grand Slams than they do on anything else; in fact, “career Grand Slams won,” which obviously ignores achievement in other events, is a commonly-used proxy for all-time ranking.

There is another problem with the Melog system that troubles me far more: It requires a great deal of time to compile the necessary data to evaluate even a single year of tennis. And rewarding as I find that process, it still means that I’ve only got the numbers for 2008-14 so far, which leaves me without a ready means of comparing Roger Federer to Pete Sampras and Bjorn Borg. That would be a nice capability to have.

As it happens, I’ve developed a method that addresses both of those problems at once: it is based entirely on Grand Slam performance, and it is readily calculable for anyone who’s played in the Open Era. And given the content of these introductory paragraphs, you’ve probably figured out that I’m presenting that method here.