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.

Read the rest of this entry »

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?

Read the rest of this entry »

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.

Read the rest of this entry »

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?

Read the rest of this entry »

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.

Read the rest of this entry »

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.

Read the rest of this entry »

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”?

Read the rest of this entry »

On the surface, Brett Gardner looks like a Bobby Abreu protege (without any power). Since 2010, Brett has shown off his great eye for pitches, posting the 2nd lowest chase rate in baseball at 18.1%.

His ability to make contact with pitches is also astonishing, as he makes contact with 97.2% with pitches in the strike zone, behind only Juan Pierre and Marco Scutaro. Of the 2789 pitches Brett has seen since the start of 2010, he has only swung and missed at 265 pitches.

Where Brett Gardner lacks is in his ability to swing at pitches in the strike zone. Over the last two seasons, Brett has swung at a major league low 45.2% of pitches in the strike zone. He owns this record almost 6% (next lowest is Elvis Andrus at 50.9%) and is almost 20% below the league average. Combined with his low chase rates, its only natural also that Brett has the lowest swing rate in MLB at 31.3%, compared to the league average of 45.6%.

Read the rest of this entry »

All major league sports division winners gain entry into the playoffs–the difference is HOW those division victors are determined. For example, the NFL places a greater weight on division record, so much so that a 8-8 division winner (like the 2010 Seahawks) is seeded higher than a wild card with a better record (like the 2010 New Orleans Saints). The NBA gives the top three seeds in each conference to the division winners, with winning the division based on overall instead of division record.

I was curious how baseball playoffs would be affected if a team’s division record determined the division winner, and I expected to see a handful of changes. I was VERY surprised with what I saw. Read the rest of this entry »

How accurate were our Steamer Projections?  Which system or combination of systems should we use to prepare for our fantasy baseball draft?  Thanks to MGL’s work we already know that Steamer had great success projecting pitcher quality in 2010 and less success projecting batter quality.  Here will we attempt to discover why some systems are having more success than others by breaking hitting and pitching performances into components.  This should also serve as a follow-up to our analysis from a year ago.

Full Disclosure

As the creators of Steamer Projections you may be justifiably skeptical of our ability to serve as an unbiased judge of projection systems.  In an attempt to allay your concerns we are making our data set available here so that anyone can check our data or pursue their own analysis.

Read the rest of this entry »