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5 Stats That Might Mean Less Than You Think

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We're living in the golden age of statistics for sports geeks. If you want to estimate how many wins Wilt Chamberlain accounted for in the 1963-1964 season, you can do that. If you want to know whether people with the initial K strike out more often, you can do that, too. Many number crunchers believe the old stats we grew up with aren't actually very helpful. Like these:

1. RBI

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Because it's such a team sport, baseball is chock full of metrics that give, at best, an incomplete picture of a player's performance. Take Runs Batted In, still one of the chief offensive statistics. Sure, the all-time RBI list is a who's who of the best hitters—Hank Aaron, Babe Ruth and Barry Bonds are the top three—but critics have long argued that it's a poor way to judge how good an individual hitter is. In The Sabermetric Manifesto, David Grabiner wrote that RBIs are not "meaningless, only incomplete" because they don't measure a batter's full offensive production. Outside of a solo home run, the only way to record an RBI is to have someone on base ahead of you, so even if you put, say, Hank Aaron onto the Miami Marlins, there just wouldn't be many chances.

2. Pitcher Wins

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But the RBI debate pales in comparison to some of baseball's long-hated pitching stats. When Felix Hernandez won the Cy Young award in 2010 despite recording just 13 wins (by contrast, last year's two winners each had 20 wins), it was hailed as a victory for the stat geeks. Among his achievements, Hernandez led the American League in ERA, quality starts, and fewest hits per nine innings, and was second in strikeouts, walks and hits per nine innings. In short, just about everything except for wins, which most saw as a reflection of the putrid Seattle Mariners team he pitched for. A great pitcher on a bad team might look unimpressive in the win column simply because his team doesn't score.

3. Points Per 48 Minutes

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Of course, the statistical noise goes well beyond baseball. Take basketball, where minutes played is still an oft-tallied and much-discussed statistic, with estimates of what a player's contribution would be over a full 48-minute game. But as Hall of Famer Charles Barkley has said, the only reason to consider what somebody would have done in 48 minutes is because they weren't good enough to play all 48 in the first place.

4. Passer Rating

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Even football—with its multi-million dollar fantasy business based entirely on statistics—has struggled to come up with a set of advanced metrics of its own. That's highlighted by the ultra-confusing quarterback passer rating, measured on a scale up to 158.3. Even the NFL has admitted the stat only rates passers, not total quarterbacks. QBR doesn't fully account for rushing plays, the offense the QB plays in, or his overall record, and the stats "do not reflect leadership, play-calling and other intangible factors," according to the NFL's own site. And while QBR has its defenders—Sports Illustrated's Kerry Byrne has pointed out that it does correlate closely with winning percentage—ESPN has worked to replace it with their Total QBR stat, which they say incorporates the context of each play to better account for the quarterback's contribution. But that has its own critics for being too confusing and for not weighing scores based on the situation.

5. Time of Possession

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Even one of the most valued NFL stats—time of possession—is being challenged. The argument went that the team who had the ball the longest was dominating on offense and "controlling the ball." But that's given way recently to a number with even more bearing on the game: points scored. New Philadelphia Eagles coach Chip Kelly, who prides himself on his fast-paced offense, recently took aim at the traditional time of possession figures, saying it was really "how much time can the other team waste?"

"Most games, we lose the time of possession, but it's how many snaps do you face?," Kelly said. "And I think in both [preseason] games we've played, we've played more snaps than our other team."

Across all sports, there are traditional metrics that may not ultimately carry much value. Fans have questioned hockey's shots on goal numbers, pointing out that shots that don't go in still don't count for anything. People talk about serve speed in tennis, but that ultimately doesn't reflect how a player reacts or plays on the surface. Golf's "Driving Accuracy Percentage" is supposed to measure the number of hits that land on the fairway, but doesn't really measure how errant a shot is or how it impacts performance.

But with so much attention on slicing and dicing every shot of every game, there's a good chance that every questionable stat will be tweaked and refined and replaced with something else. That is, until something more advanced comes along to replace them.


Game Changers: Real. Sports. Data.

A recurring web series on sports and big data, featuring industry experts and social commentary. For more information, visit ibm.com/sports.

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History
A Brief History of Deep Blue, IBM's Chess Computer
Stan Honda // AFP // Getty Images
Stan Honda // AFP // Getty Images

On July 29, 1997, IBM researchers were awarded a $100,000 prize that had gone unclaimed for 17 years. It was the Fredkin Prize, created by Carnegie Mellon University (CMU) professor Edward Fredkin in 1980. An artificial intelligence pioneer, Fredkin challenged fellow computer scientists to create a computer that could beat the best human chess player in the world. That's exactly what Deep Blue did in May, 1997.

It was an extremely long road to victory. After Fredkin's initial challenge in 1980, a team from Bell Labs created a chess computer in 1981 that beat a chess master. In 1985, Feng-hsiung Hsu created ChipTest, a chess computer that set the stage for later efforts.

By 1988, a CMU team including Hsu created a system that beat an international master. That one was called "Deep Thought," named for the computer in The Hitchhiker's Guide to the Galaxy—a fictional computer spent 7.5 million years calculated "the Answer to The Ultimate Question of Life, the Universe, and Everything." (That answer, of course, was 42.)

Deep Thought underwent additional development at IBM, and in 1989 it went head-to-head with Garry Kasparov, who is widely considered the best chess player of all time. Kasparov destroyed the machine in a two-game match. Here's the first part of a documentary about Deep Thought, which helps set the stage for Deep Blue:

Deep Thought eventually led to Deep Blue, an IBM project led by Hsu, along with his former Deep Thought collaborator Murray Campbell, among others.

The computer science problem of chess is deep. First the machine needs to understand the state of the board—that's relatively easy—but then it needs to predict future moves. Given that the 32 pieces on the board are capable of moving to a variety of other positions, the "possibility space" for the next move (and all subsequent moves) is very large.

In theory, a sufficiently beefy computer could simulate every possible move (and counter-move) in its memory, rank which moves end up doing best in each simulated game, and then perform the optimal move on each turn. But to actually implement a computer that powerful—and fast enough to compete in a time-limited tournament—was a matter of extreme effort. It took Hsu more than a decade to master it.

Six men pose with a chess board and timer. On one side of the board, a sign reads Garry Kasparov. On the other side, a computer keyboard and monitor represent Deep Blue.
The IBM Deep Blue chess computer team poses in May, 1997. From left: Chung-Jen Tan (team manager), Gerry Brody, Joel Benjamin, Murray Campbell, Joseph Hoane and Feng-hsiung Hsu (seated).
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On February 10, 1996 in Philadelphia, Deep Blue went head-to-head with Kasparov, and Kasparov beat the computer handily. Though Deep Blue scored one winning game and two draws, it lost three games to Kasparov outright. Deep Blue did set a record for winning that one game, but it needed the match to earn the Fredkin Prize.

By this point, Kasparov was used to destroying chess computers, and the media lapped it up—this was a man-versus-machine story for the ages. By May 1997, IBM had heavily upgraded Deep Blue (some called it "Deeper Blue") with vastly improved computing resources, preparing for a rematch. When that rematch came, Kasparov would face a worthy opponent.

On May 11, 1997 in New York City, the upgraded Deep Blue entered the match with a large, excited audience. Kasparov won the first game, but Deep Blue took the second, tying the players. Then came three games that ended in draws. In the sixth game, Kasparov made a mistake in the opening. Deep Blue won that sixth game quickly, winning the match, much to the astonishment of the crowd. Kasparov asked for a rematch. The Deep Blue team declined.

Kasparov claimed to have perceived a human hand in Deep Blue's play. Kasparov wondered whether a human chess player was somehow feeding the computer moves, much like the infamous Mechanical Turk of yore. Various conspiracy theories flourished, but came to nothing.

When the Fredkin Prize was awarded to Hsu, Campbell, and IBM researcher A. Joseph Hoane Jr., Fredkin told reporters, "There has never been any doubt in my mind that a computer would ultimately beat a reigning world chess champion. The question has always been when." Hsu told The New York Times, "Some people are apprehensive about what the future can bring. But it's important to remember that a computer is a tool. The fact that a computer won is not a bad thing."

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What Tennis Shoes Looked Like in the Early 1900s

Mental_floss co-founder Mangesh Hattikudur is at the US Open today. Between matches, he'll be serving up some tennis history and random knowledge.

Image credit: whatsalltheracquet.com

In the pre-Swoosh era, the best shoes for lawn tennis had giant treads and looked like they could be worn to church.


Follow ibmsports on Instagram for scenes from the U.S. Open.

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