The DRaysBay Stats Guide
by R.J. Anderson on Jan 26, 2009 2:09 PM EST
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Pitching
Win/Losses/Saves
Absolutely have zero value. If Scott Kazmir is on the mound, goes five, gives up five runs, and has his team bomb the opposing starter for seven, he's in line for a win. If James Shields pitches eight, gives up a lone run, and the opposing starter shuts the Rays down, Shields might get charged with a loss. Troy Percival can walk the bases loaded, allow two sac flies, and as long as he kept the lead, get credited with a save. W/L/S do not speak for a players actions, but rather his team actions. Starters on good teams will probably have more wins than starters on poor teams, the same goes for saves.
ERA
The most common pitching stat around. We use ERAs scale (runs allowed per nine innings) for other run average metrics like tRA and FIP, but ERA itself isn't forth telling about pitcher value. Luck, like poor defense or a shoddy non-error call, can inflate ERAs. Plus, pitchers don't control hit rates nearly as much as people think, which is also why WHIP is pretty meaningless. ERA+ is equally uninformative.
Strikeouts
Are good, very, very good. Swinging strikeouts are the best since they imply the pitcher has plus stuff or velocity and are seemingly more sustainable than strikeouts looking - which relies on an off hitter and a potentially favorable strikezone. Strikeouts usually translate well from year to year and minors to majors.
Walks
Are bad, very, very bad. For high strikeout pitchers they're more acceptable, given the ball will be put into play less, but for average to below average strikeout types, they can be killer.
LD/GB/FB
Or line drives, groundballs, and flyballs. If you're ranking them in terms of what you'd prefer: GBs >> FBs >>>> LDs. Liners are the batted ball type most likely to go for a hit. Pitchers who give up a ton of line drives simply don't make it to the majors, or if they do, they have a freaking warehouse of rabbits feet. Flyballs can result in homeruns, the more flyballs, the more homeruns (depending on the park). Yeah, groundballs can go for hits, but how often is a grounder going for extra bases?
FIP
Fielding Independent Pitching. Okay, here's where the whole "isolating the pitcher from the team" process gets started. The pitcher can control three things: walks, strikeouts, and homeruns allowed. Hence, FIP takes each of those into account and puts it on a scale so that league average FIP is equal to league average ERA. FIP+ is the "compared to league average" form.
tRA
Graham's stat is FIP on hGH. The pitchers are charged not only for homeruns, walks, and strikeouts, but also the batted ball type. So pitchers with a ton of line drives allowed are going to have a higher tRA than pitchers with mostly groundballs. tRA* is the regressed form, and tRA+ is the "compared to league average" form.
BABIP
This plays hand and hand with LD/GB/FB. Most pitchers have batting averages on balls in play between .290 and.310. Anything on the other end of the extremes, without something peculiar involved with the LD rate, is probably going to regress back to that range. Are there some outliers? Absolutely. Good/bad defense can affect this number heavily. A better point of reference than "Batting Average Against", but neither tells you much about the pitchers talent. The best method for a quick BABIP estimator is an average over the last three years.
Hitting
Batting Average
Only tells one side of the story. A player can hit .310, but if his OBP and SLG are .320/.400, he's not quite as useful as you would assume.
On-Base Percentage/Average
Tells a slightly larger side of the story. It's usually best to combine OBP with walk rates and or an iso discipline stat (which is to say OBP - BA).
Slugging Percentage
Again, tells one part of the story, the bases per at-bat side.
wOBA
Don't let the name confuse you, this isn't just OBA/OBP under a different name. Instead it's a Tom Tango created metric that uses linear weights (aka: weighs how much each event contributes to a run) assigns a value to, and then spits out in a form that we can easily turn into runs created. FanGraphs version takes into account SB and CS as well.
wRAA
FanGraphs version of wOBA runs created above league average. The "Value" section of a player page includes a park adjusted version of this metric.
BABIP
See above.
LD/GB/FB
See above.
RBI
Oh heavens to Betsy I hate RBIs. People will look at a player's runs batted in and assign labels like "run producer" to him. Perfect example: Jose Guillen. Look, the guy hits for some power, but he's not a very good hitter, especially not for a corner outfielder. In 2008 Guillen had 97 RBIs, yet had a wRAA of -5. Yeah, a below average hitter had nearly 100 RBIs.
Homeruns
They're nice, but they aren't the only way to score. Also, a player who hits 30 homeruns and does nothing else is not valuable, but a player who hits 5 homeruns and does other stuff can be.
Stolen Bases
Are nice. Just make sure you're succeeding ~70% of the time.
Defense
Errors
Too opinion based, plus if a player doesn't have the range to get to a ball, how can it be an error?
Fielding Percentage
Consider the above. If Jason Bartlett gets to a ball deep in the hole, then throws the ball away, that's an error. If Derek Jeter takes two steps, then lets the ball shoot through the gap while blowing bubbles, that's not an error. You see the problem here.
UZR
FanGraphs defensive stat of choice. Developed by MGL. Takes the park into equation and adjusts based on such.
CHONE
A defensive system developed by Rally/Sean Smith.
PMR
A defensive system developed by David Pinto.
Plus/Minus
A defensive system developed by John Dewan and The Fielding Bible group. Provided in PLAYS form, so multiple that by 0.8 (the value of turning a single into an out) and you'll get the runs form. These are formed by a team of experts watching each play and giving a "+" or a "-" based on the play made. If Bartlett makes a play that another shortstop missed, he gets a "+", if Bartlett misses a play another shortstop made, he gets a "-".
Use these together, average them, and you'll have a nice idea of how valuable (or how not valuable) a player is.
Defensive spectrum:
DH | 1B | LF | RF | 3B | CF | 2B | SS | CA
The left side is the easier positions, the right the harder.
Win Values
Positional adjustments:
CA - 22.5 runs.
1B/COF - -7.5 runs
2B/CF/3B - 2.5 runs
SS - 7.5 runs
DH - -22.5 runs, but since batters are usually ~ -5 runs worse as a DH/pinch hitter, we give them a five run bonus, so only penalize the player -17.5 runs.
Replacement adjustments:
This is a fancy way of saying: a replacement level player is ~20-22.5 runs worse than an average player. Think Ray Olmedo.
Replacement player
Average player: replacement player :: superstar player : average player
Batting
Park adjusted wRAA.
Fielding
UZR totals.
Value Runs
The total runs the player contributed.
Value Wins
Value Runs/10 (or the amount of runs for a "win")
pLI
Leverage, aka how tough or untough a situation is for a pitcher. 1 is the standard for starting pitchers, 1.8-2 for closers, 1.3-1.5 for set-up men, ect, and 0.5 or so for mop-up men.
WPA
Win probability added, usually found next to the win expectancy chart, seen below.
Projection systems
PECOTA
The best of any projection system. This is a Baseball Prospectus feature.
ZiPS
Found at BaseballThinkFactory. Pretty good.
CHONE
Found at baseballprojection.com. Again, pretty good.
Marcels
The most basic of all projection systems, yet still does pretty decent considering it's basically the last three years of data, an age modifier, and a ton of regression. All other projection systems should aim to outperform Marcels or otherwise quit.
Analysts to trust
Keith Law
Rob Neyer
Dave Cameron
Tom Tango
MGL
Sky Kalkman
Peter Bendix
Graham MacAree
Matthew Carruth
Jeff Sullivan
Eric Seidman
Anyone at BTB or FanGraphs
Miscellaneous
A great defender CAN be as valuable as a great hitter can.
Position DOES matter when evaluating offense.
Pitchers do not follow the same aging/prime curve as hitters.
Things like vet presence, leadership, great chemistry have marginal effects on performance.
Most hitters are better at home than the road.
One year of offensive data = three years of defensive data.
Consider each data point on its own. So, if Player A has UZRs of 5, 0, -5, that doesn't mean he's on the decline. Use a three year average (and add weight to the most recent results if you wish) instead.
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Comments
I would also edit to explain the defensive stats better
At the very least, how they;re measured…
Vogt early, Vogt often.
And the projections as well
To a reader not versed in these stats, it wouldn’t be immediately apparent to them what the numbers actually mean from what you gave.
Vogt early, Vogt often.
What do you mean the projections?
The systems?
by R.J. Anderson on Jan 26, 2009 2:26 PM EST up reply actions
If someone has a CHONE score of 38.3
What does that mean? It’s not apparent from what you wrote here.
Vogt early, Vogt often.
Wait.
http://www.baseballprojection.com/TBA2009.htm
The R150 column, or something else? Defense?
by R.J. Anderson on Jan 26, 2009 2:29 PM EST up reply actions
More Reference
Example: UZR (ultimate zone rating): The number of runs above or below average a fielder is in both range runs and error runs combined.
Example:FIP
Fielding Independent Pitching, a measure of all those things for which a pitcher is specifically responsible. The formula is (HR*13+(BB+HBP-IBB)*3-K*2)/IP, plus a league-specific factor (usually around 3.2) to round out the number to an equivalent ERA number. FIP helps you understand how well a pitcher pitched, regardless of how well his fielders fielded. FIP was invented by Tangotiger.
Gives you the basic purpose of the stat.
Heavy on BP terms, but does have some generic terms like BABIP
StatCorner's Glossary
http://statcorner.com/glossary.html
Includes league averages.
by R.J. Anderson on Jan 26, 2009 2:34 PM EST up reply actions
Stolen Bases
Are nice. Just make sure you’re succeeding ~70% of the time.
What makes 70% the magic number? I trust that is a good metric, but I haven’t seen any stat to back up the 70% rule.
.
http://www.tangotiger.net/bsrexpl.html
Linear weights value.
by R.J. Anderson on Jan 26, 2009 2:43 PM EST up reply actions
Great stuff
wOBA a bit unclear, but i’m trying
Check this out
http://www.insidethebook.com/woba.shtml
Instead of trying to take two statistics (OBP, SLG) and combine and correct their flaws in the hopes of getting one number, we prefer to start from scratch. Furthermore, by recasting the number onto the OBP scale, it makes it much easier for the reader to get a grasp on the number. wOBA is weighted on-base average (we call it an average rather than a percentage). When you look at wOBA numbers throughout the book, just think OBP, and you’ll be fine. In other words, an average hitter is around 0.340 or so, a great hitter is 0.400 or higher, and a poor hitter would be under 0.300.
by Tommy Rancel on Jan 26, 2009 4:00 PM EST up reply actions
Question on Sonny
Why, with one of the best defenses in the league, did there remain a disparity between his ERA and TRA/FIP. And if he never approaches an ERA close to either his FIP/TrA, is this a problem with him, or the model?
Not trying to start a flame war, just curious as to what you guys think?
by GomesSweetGomes on Jan 26, 2009 3:33 PM EST reply actions
Also
What do you make of guys like Kaz who seem to consistently outperform their FIP and TRA #’s? I know results-based analysis is frowned upon, but at some point the model should give way. Your thoughts?
by GomesSweetGomes on Jan 26, 2009 3:35 PM EST up reply actions
Pitchers "outperform" or "underperform" their FIP/tRA when you compare it to ERA all the time.
http://www.fangraphs.com/leaders.aspx?pos=all&stats=pit&lg=all&qual=y&type=1&season=2008&month=0
FG has a stat “E-F” which is ERA minus FIP. Check through there to see if you find any sort of “skill” when it comes to over/underperforming it, but basically: run distribution is pretty random.
by R.J. Anderson on Jan 26, 2009 3:44 PM EST up reply actions
I would expect Sonny to underperform it in 2007
But not last year.
Also Santana, Zambrano, and Hamels all seem to consistently over-perform. I think I will take some time and look at these #’s and get back to you.
by GomesSweetGomes on Jan 26, 2009 3:57 PM EST up reply actions
Dice-K and Sonnanstine are good examples
Dice-K is never as good as his ERA because he walks so much and conversely Sonnanstine is not as bad as his ERA because ERA accounts to many factors that he can’t control.
by Tommy Rancel on Jan 26, 2009 4:03 PM EST up reply actions
At least until HitF/x comes out.
And either proves or disproves Sonny’s balls aren’t hit harder.
by R.J. Anderson on Jan 26, 2009 4:05 PM EST up reply actions
If BIS and STATS can employ banks of analysts
to try to create better defensive data, why don’t we have or discuss more info on batted ball speed?
Clearly that will vary by type, and especially so on ground balls, perhaps necessitating ground contact metrics as well – a two hopper isn’t the same as a slow roller. Since I’m not such a digit head (no discredit implied to anyone) I don’t care to peruse the data, but studying the analysis could be fascinating. My sense is that if there is variability in batted ball speed – clearly, higher LD rates would mean a higher average, so perhaps speed by type is more illustrative, particularly on GB’s – this might serve as a bridge between a heavy reliance on FIP and some attempt to measure a pitcher’s possible influence beyond batted ball type. Or it could be totally inconclusive.
To me speed of batted ball is one of the key variables – a relatively easy thing to calculate it would seem. The data is probably available, I’m just not in the know. But I’ve not seen it discussed in depth.
I’m just not a fan of the attempt to dismiss or wash “luck” out of statistical systems. Variability is one of statistics’ key tenets. Projection systems sometimes include ranges – studying where players and even teams fall relative to these bell curves could go a long way towards explaining individual and team performance. And since they’re based on the available data, future results should move the projection medians in the future. Outlying performances are a part of what makes success. Regression is what restrains wise folks from going all bandwagon on improvements. But it’s the outliers that are the most interesting to view.
by nyyfaninlaaland on Jan 26, 2009 4:40 PM EST up reply actions
The data is soon coming.
As in this year. It’s called HitF/x, the preliminary data is simply going to measure the speed of the ball off the bat. The people smarter than myself can use that to figure out a lot of things.
by R.J. Anderson on Jan 26, 2009 4:43 PM EST up reply actions
That could result in a whole lot of change
in pitcher perceptions.
Or it could be much ado about not much. Let’s hope it ’s the former.
I think it will ultimately have to be broken into batted ball types, travel distance, and speed when reaching fielder positioning, etc. Could open up a lot of avenues for study.
by nyyfaninlaaland on Jan 26, 2009 4:58 PM EST up reply actions
I'm hoping the former.
But we’ll see.
Pitching/hitting stats should be helped by it. Fielding stats, ehh, not until they do a full field grid, which isn’t happening anytime soon, or ever.
by R.J. Anderson on Jan 26, 2009 5:01 PM EST up reply actions
Oh, and RJ... BPIP?
I guess given the fluctuation of WHIP, the BPIP (bases per IP) thing never really took off either.
Did it better predict ERA measures though? I’d expect so, even given it’s likely variablity from season to season. As you rightly point out, if a stat varies, it needs be viewed in larger sample slices.
For those who don’t recall this, RJ (maybe I sucked him in on this one a bit, and let him and others do all the lifting) worked on this stat concept for pitcher performance that we envisioned might be more correlated with ERA measures than WHIP is -and that’s a fairly good one as I recall. The underlying thought being that since it requires allowing 4 bases for a run (and runs are how the game is scored and won), a pitcher surrendering more bases than simply baserunners (WHIP) will surrender more runs. It tends to project slugging onto WHIP, and could be refined to include defensive and offensive add-on metrics like DP’s (as a reducing factor), SB’s (an increasing factor), CS (reducing), SF’s, Sacs (more complicated, but using linear weights would help across the board), etc. It also included HBP’s (WHIP doesn’t for some reason), etc.
by nyyfaninlaaland on Jan 26, 2009 4:55 PM EST up reply actions
Top 10 E-F for 2008, if you're a good pitcher then you're a good pitcher by any metric
Tim Lincecum
Cliff Lee
CC Sabathia
Dan Haren
Roy Halladay
Josh Beckett
Derek Lowe
Brandon Webb
by Tommy Rancel on Jan 26, 2009 4:05 PM EST up reply actions
you forgot to put me in there
In the analyst not to trust for statistics
by therayspartyleader on Jan 26, 2009 3:39 PM EST reply actions
Can there be a permalink for this thing on the front page or something?
I’d love to be able to refer to it whenever…
B Rad the Ray Fan
There is now.
Look at the logo, now scroll down, and right before you hit the RotoWorld sidebar thing, you should see “Reference Materials”.
by R.J. Anderson on Jan 26, 2009 4:07 PM EST up reply actions
RJ can you do me a huge favor
In an ABC format, can you give me an example of how WAR is formulated? Like here is Longoria’s wOBA, his UZR, or whatever, what is the simple math that goes into getting the WAR? Thanks.
by BossmanJunior333 on Jan 26, 2009 5:58 PM EST reply actions
I think I can.
Basically: you take his wOBA. Subtract the league average wOBA from that. Divide that by 1.15, then multiply it by the number of plate appearances. That gives you offensive runs above average. Park adjust that number, and you have your offense.
Take his UZR, add that, you have his offense and defense. Take his position, add that, then add his replacement level in, and multiply that by playing time (FG does this for each step rather than at the end, but you get the same)
So:
20 offensive runs
13 fielding runs
22.5 replacement
2.5 positional
_
58 runs
He played in ~85% of the time, 0.84*58 = 49. Divide by 10 and we get 4.9 WAR. FG has him at 5.1, so I’m two runs off somewhere, but that’s the idea.
by R.J. Anderson on Jan 26, 2009 6:08 PM EST up reply actions
Only a couple quick questions
Im guessing fangraphs gives you league average wOBA, right?
How do park adjust the number?
As for playing time, do you take the percentage of games he played in/started? I did the backwards math and 85% would be 137.7 games, but he only played in 122. Maybe you just guessed on that number. 122 games would be 75 percent. Which would result in a 4.35 WAR. So yeah, im confused haha.
by BossmanJunior333 on Jan 26, 2009 6:59 PM EST up reply actions
league average wOBA is defined to be league-average OBP
Beyond the Boxscore // Calling BJ Upton lazy is lazy.
It should be:
(13+11.5+2.5)*.84=31.92
31.92+19.9/10=5.182
I don’t know what else is missing, but that gets it closer.
on fangraphs it says his replacement level is 16.9
by BossmanJunior333 on Jan 26, 2009 7:04 PM EST up reply actions
then what is the 11.5 rglass used?
I understand the 13 (defense), 2.5 (positional), and 19.9 (offense), but whats the 11.5? And why is the replacement level adjusted (to 84 %) but the offensive and defensive arent?
by BossmanJunior333 on Jan 26, 2009 7:07 PM EST up reply actions
The offensive/defensive are adjusted.
UZR is based on the amount of innings played.
wRAA is adjusted based on the amount of PAs.
That’s what I neglected to realize when I did my original piece.
The only thing you have to playing time adjust from those FG numbers is positional (2.5) and replacement (20). So:
19 offensive runs + 13 defensive runs + (0.84*(2.5+20)) = 51
by R.J. Anderson on Jan 26, 2009 7:10 PM EST up reply actions
Dave:
As Sean showed in his article, and has been shown elsewhere, the expected value of a replacement level player is about negative 20 runs per 600 PA. Or, to phrase it a bit differently, if you lost a league average player and replaced him with a freely available guy, you’d lose about two wins. That’s why the replacement level calculation in our Win Value formula is 20/600*PA. If you get exactly 600 PA during a season, your replacement level adjustment will be +20 runs. If you get 700 PA, your replacement level adjustment will be +23.3 runs. The more you play, the higher the replacement level adjustment, because you’re filling a larger quantity of playing time and that chunk won’t need to be filled by anyone else.
http://www.fangraphs.com/blogs/index.php/win-values-explained-part-four/
by R.J. Anderson on Jan 26, 2009 7:08 PM EST up reply actions
oh ok
so shouldnt it still be the 16.9 that fangraphs indicates for his replacement level? I still dont get the 11.5
by BossmanJunior333 on Jan 26, 2009 7:11 PM EST up reply actions
WHERE IS CLUTCH!
Top Josh Paul Pornos- Big Navi Stroking, 2pitchers1cup, BJ to the Balls, Riggans Your Thingans
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