A call for better statistics to better distinguish between competitive gamers

Here is a call for more statistics in gaming, which would help understand the techniques of and differentiation between competitive gamers:

Some people even believe that competitive gaming can get more out of stats than any conventional sport can. After all, what kind of competition is more quantifiable than one that’s run not on a field or on a wooden floor but on a computer? What kind of sport should be able to more defined by stats than eSports?

“The dream is the end of bullshit,” says David Joerg, owner of the StarCraft statistic website GGTracker. “eSports is the one place where everything the player has done is recorded by the computer. It’s possible—and only possible in eSports—where we can have serious competition and know everything that’s going on in the game. It’s the only place where you can have an end to the bullshit that surrounds every other sport. You could have bullshit-free analysis. You’d have better conversations, better players, and better games. There’s a lot of details needed to get there, but the dream is possible.”…

“There are some stats in every video game that are directly visible to the player, like kill/death,” GGTacker’s Joerg said. “Everyone will use it because it’s right in front of their face, and then people will say that stat doesn’t tell the whole story. So then a brave soul will try to invent a stat that’s a better representation of a player’s value, but that leads to a huge uphill battle trying to get people to use it correctly and recognize its importance.”…

You could make the argument that a sport isn’t a sport until it has numbers backing it up. Until someone can point a series of statistics that clearly designate a player’s superiority, there will always be doubters. If that’s true, then it’s true for eSports as much as it was for baseball, football and any other sport when it was young. For gaming, those metrics remain hidden in the computers running StarCraft, League of Legends, Call of Duty and any other game being played in high-stakes tournaments. Slowly, though, we’re starting to discover how competitive gaming truly works. We’re starting to find the numbers that tell the story. That’s exciting.

This is a two part problem:

1. Developing good statistics based on important actions with a game that have predictive ability.

2. Getting the community of gamers to agree that these statistics are relevant and can be helpful to the community.

Both are complex problems in their own right and this will likely take some time. Gaming’s most basic statistic – who won – is relatively easy to determine but the numbers behind that winning and losing are less clear.

Using algorithms for better realignment in the NHL?

The NHL recently announced realignment plans. However, a group of West Point mathematicians developed an algorithm they argue provides a better realignment:

Well, a team of mathematicians at West Point set out to find an algorithm that could solve some of these problems. In their article posted on the arXiv titled Realignment in the NHL, MLB, the NFL, and the NBA, they explore how to easily construct different team divisions. For example, with the relatively recent move of Atlanta’s hockey team to Winnipeg, the current team alignment is pretty weird (below left), and the NHL has proposed a new 4-division configuration (below right):

Here’s how it works. First, they use a rough approximation for distance traveled by each team (which is correlated with actual travel distances), and then examine all the different ways to divide the cities in a league into geographic halves. You then can subdivide those portions until you get the division sizes you want. However, only certain types of divisions will work, such as not wanting to make teams travel too laterally, due to time zone differences…

Anyway, using this method, here are two ways of dividing the NHL into six different divisions that are found to be optimal:

My first thought when looking at the algorithm realignment plans is that it is based less on time zones and more on regions like the Southwest, Northwest, Central, Southeast, North, and Northeast.

But here is where I think the demands of the NHL don’t quite line up with the goals of the algorithm to minimize travel. The grouping of sports teams is often dependent on historic patterns, rivalries, and when teams entered the league. For example, the NHL realignment plans generated a lot of discussion in Chicago because it meant that the long rivalry between the Chicago Blackhawks and the Detroit Red Wings would end. In other words, there is cultural baggage to realignment that can’t only be solved with statistics. Data loses out to narratives.

Another way an algorithm could redraw the boundaries: spread out the winning teams across the league. What teams are really good tends to be cyclical but occasionally leagues end up with multiple good teams in a single division or an imbalance of power between conferences. Why not spread out teams by records which then gives teams a better chance to meet in the finals or other teams in those stacked divisions or conferences a chance to make the playoffs?b

Scorecasting looks at data: Cubs not unlucky, just bad

The recently published book Scorecasting (read a quick summary here) has a chapter that tackles the question of whether the Chicago Cubs are cursed or not. Their conclusion after looking at the data: the team has simply been bad.

But how can anyone disprove the existence of a curse? According to the authors, teams that frequently field good teams but finish in second place, or make the playoffs but fail to win a title, justifiably can claim to be unlucky. So, too, can teams that have impressive batting, hitting and defensive statistics, but whose strong numbers don’t translate into victories.

On both scores, the Cubs proved to be “less unlucky” than the average team. That is, not unlucky, just bad.

“Relative to other teams, we could easily explain the Cubs lack of success from the data — both their on the field statistics and where they finished in the standings,” Moscowitz said.

Since their last Series appearance in 1945, the Cubs have finished second fewer times than they have finished first. They also have finished last or next to last close to 40 percent of the time. According to the book, the odds of this happening by chance are 527 to 1.

The authors of “Scorecasting” believe that what has been stopping the Cubs the last three decades is the extreme loyalty of their fans, which has served to reduce the incentive for Cubs management to win.

According to their analysis, which is primarily based on attendance records and the team’s won-loss percentage from 1982-2009, Cubs fans are the least sensitive to the team’s winning percentage, while White Sox fans are among the most sensitive.

There are two interesting arguments going on here, both of which commonly come up in conversation in Chicago:

1. The data suggests that the Cubs have just been a bad team. It is not as if they have reached the playoffs or World Series multiple times and lost. It is not that they have impressive statistics and this hasn’t translated into wins. They just haven’t been very good. It would be interesting to read the rest of this chapter to see if the authors talk about the MLB teams that have been truly unlucky. I don’t know if a chapter like this will put the talk of a Cubs curse to rest but it is good to hear that there is data that could quiet the curse talk. (But perhaps the curse is what Cubs fans want to believe – it means that the team or the fans aren’t at fault.)

2. Cubs fans like to think that they are loyal while White Sox fans argue that Cubs fans will go to Wrigley Field no matter what. So is the answer for more Cubs fans to stay away from the ballpark until the team and the Ricketts show that they are serious about winning?