Stated in as simplest terms as possible, the goal of pitching is to get batters out without allowing runs to score. There are three ways any given pitch can get a batter out. A pitch can either be swung on and missed, taken for a called strike, or batted in such a way that the batted ball does not result in the runner reaching base. Batted balls involve the defence and are therefore less directly related to the pitch’s effectiveness at getting outs. That leaves us with swinging strikes and called strikes as the two best ways to measure a pitch’s effectiveness.

In Part I of my research on curveballs, I looked at what makes a curveball effective from a swinging strike perspective. I used an outcome variable that I like to call: ratio of effectiveness. Ratio of effectiveness is simply a ratio between swinging strikes and home runs hit. In Part II of my research, I will look at the effectiveness of curveballs from a called strike perspective. This work will aim to answer two basic questions: 1) are curveballs taken for strikes more often than fastballs? And 2) what are the characteristics of curveballs most often taken for strikes?

Are curveballs taken for strikes more often than fastballs?

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Velocity, location, and movement are all unquestionably important when we try and compare ‘good’ pitches to ‘bad’ pitches. My particular interest lies in how important each are. I’ve often wondered if a 98 mph cutting fastball can be thrown right down the middle and still have little chance of being hit for a home run? Or conversely, is an 88 mph fastball that’s straight as an arrow still likely to get a swinging strike if it paints the bottom outside corner of the zone?

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This article is the second of a two part series evaluating 2011 baseball player forecasts. The first looks at hitters and found that forecast averages outperform any particular forecasting system. For pitchers, it appears as though the results are somewhat reversed. Structural forecasts that are computed using “deep” statistics (k/9, hr/fb%, etc.) seem to have done particularly well.

As with the other article, I will look at two main bases of comparison: Root Mean Squared Error both with and without bias. Bias is important to consider because it is easily removed from a forecast and it can mask an otherwise good forecasting approach. For example Fangraphs Fan hitter projections are often quite biased, but are very good at predicting numbers when this bias is removed.

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This article is an update to the article I wrote last year on Fangraphs.

This year, I’m going to look at the forecasting performance of 12 different baseball player forecasting systems. I will look at two main bases of comparison: Root Mean Squared Error both with and without bias. Bias is important to consider because it is easily removed from a forecast and it can mask an otherwise good forecasting approach. For example, Fangraphs Fan projections are often quite biased, but are very good at predicting numbers when this bias is removed.

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With apologies to Michael Lewis, what if everything you thought you knew about baseball was wrong? As our collective understanding of advanced statistical analysis in baseball grows exponentially with each passing day, we are now among a generation of baseball fans that has done more critical thinking about and retained more esoteric knowledge of the game than our parents could ever have dreamed of. Anyone who has seen MLB Network’s show on the evolution of statistics would think that between Henry Chadwick’s invention of the box score and Branch Rickey’s hiring of Alan Roth as a statistician, baseball fans in the 20th century consumed baseball metrics in only the most rudimentary of ways — via the dreaded batting average, home runs and RBI triumvirate.

However, what if I told you that one of the most advanced analytical discoveries — one that sabermetricians hold near and dear to their hearts — was actually discovered before Babe Ruth ever played a game?

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This post was born out of a conversation randomly started by Ronit Shah on Twitter. He asked me:

“Is there a more interesting player page than this one? #CoorsEffect”

The page links to Dante Bichette and I did a little research, eventually concluding that Bichette’s slash line was .360/.397/.642 at home and .268/.303/.431 away from 1993 to 1999. He also had a HR/PA of 0.06 at home versus only 0.03 away and hit 1.5 times more extra base hits at home than he did on the road.

Satchel Price decided to chime in, asking the question I asked in the title: “Has any player ever benefited more from triple crown stats? Bad defense, rarely walked, played in Coors during the steroid era.”

I was interested to see what I could find on this, so I made up an analytical tool. I downloaded all player careers for all qualified batters from Fangraphs. Then I found the average and standard deviation for home runs, runs batted in and batting average for all of these players.

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From Opening Day to April 20th, Red Sox pitchers posted a 7.14 ERA when Jarrod Saltalamacchia was behind the plate versus a 2.40 ERA when Jason Varitek started. The resulting hubbub about this split made one fact extremely clear, when comparing the influence of different catchers, sample size is really really important.

Already by June 24th, Varitek and Salty’s split has been greatly reduced, with pitchers now throwing a 3.44 ERA to the veteran captain and a 4.36 ERA to the new guy. I would bet that these numbers will continue to converge as the season drags on, but even after 182 games it’s unlikely that either catcher will have enough innings to statistically test whether one is calling a better game. This is the difficulty of assessing catcher performance: comparing catchers between teams is near impossible (because the pitching staffs are different), and comparing catchers within teams is difficult (because sample sizes are small and different pitchers use different catchers). Nevertheless, many still believe that catchers do influence pitcher performance. Where can we find the data to support this hypothesis?

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This article was originally published on WahooBlues.com.

No one likes to travel. Vacations are great and changes of scenery can be nice, but that doesn’t make the cramped bus trip or the bumpy plane ride any more pleasant. The destination may be worth it, but when was the last time you stood up after an hours-long voyage with your good mood still fully intact?

This sentiment is shared even by multimillionaires who make their livings playing a children game and are cheered by thousands of adoring fans every time they go to the office. In baseball, “getaway day” is dreaded, and while jet lag alone wouldn’t make the Indians fall to the White Sox (who’d’ve thought that would be a good example this year?), a team that just got in after a long flight is seen as being at a real, if relatively small, disadvantage at the start of a series.

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This article was originally published on WahooBlues.com.

Everyone knows that money matters in baseball. I’m a Cleveland fan, so you don’t need to convince me that my small-market Indians are at an unfair disadvantage when competing against teams like the Yankees and the Bank of Steinbrenner (in the immortal words of Ken Tremendous, “It’s like Scrooge McDuck’s gold coin-filled pool”). There’s no question that franchises with the financial flexibility to retain their stars, import new ones, and remain contenders even when their well-paid players bust have a leg-up from the get-go.

But we all know that money isn’t everything. Omar Minaya and the New York Mets gave us a years-long crash course in what happens large payrolls are spent poorly. Meanwhile, plenty of underfunded teams have had success, including last year’s Rangers and the Rays of both 2008 and 2010. Check my facts on this, but I’m pretty sure one or two low-budget Oakland A’s teams have had some mild success in the past too.

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Often I would like to have an estimate of a player’s true talent in a past year. Projection systems are always only focused on predicting future performance based on past results, but what I wanted was the best estimate of the expected performance for a player in a given year, based on his results in that year and the surrounding years.

I wanted to find suitable weights to assign to performance in the given year, plus the years immediately before and after, and have the right amount of regression to the mean. But I kept running into the same mental block; how to assign a weight to the given year’s performance, since that is exactly what I am trying to “predict”?

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