When
I write my blogs I tend to use a lot of stats that many people aren’t
accustomed to seeing. BABIP, FIP,
xFIP, K/9, OPS+, UZR and WAR are examples of stats called sabermetrics or
advanced stats. The idea of
sabermetrics is to take what a player is doing statistically and gain insight
on the player’s tendencies. These
can range form seeing what a player likes to swing at and where he likes to
swing the most using a Swing % in and out of the zone or seeing if a pitcher is
getting a little lucky do to having a stronger defense than another pitcher
using FIP and xFIP. Or simply
looking at an existing stat like OBP (On Base Percentage) and giving it more
importance by using wOBA or weighted On Base Average to see how a player does
on getting on base among other things.
In this blog I want to go over some of the saber stats I use when
looking at players and also to help people who are lost when reading the blog
and I mention a stat.
The
way I’ve decided to work this is by showing how to calculate the stat and then
give a description on how it’s relevant.
I’ll also separate them into pitching and hitting stats that way it will
be easier to know which stat pertains to which aspect of the game. Yes it is true that many of the stats
actually work for both (as an example BABIP has a hitting and pitching
calculation) but unless I say it twice it will only apply to the aspect I
mention it under. So here we go,
lets start at the hitting stats first.
Hitting:
1)
BABIP or Batting Average on Balls in Play-
Hits-Homeruns/At Bats-Strike outs-Walks-Homeruns.
Basically this is a stat that tries to gauge the “luck”
factor for hitters and literally translates out into “What is the players
batting average when he puts the ball in play.” As an example lets use Albert Pujols and Mike Trout; lets
say both hit a hard grounder into the gap between 3rd base and
Shortstop. Trout runs hard and
beats a tough throw and his BABIP goes up but on that same play Pujols is
thrown out meaning his BABIP will go down. A lot of variables can be in play and that’s what BABIP
tries to show us.
2)
OPS or On Base Plus Slugging- OBP+SLG. This is a conflicting stat and also a
misleading one. It combines two
important stats that have very good information but they aren’t equal
stats. Most consider OBP to be
more important than SLG and many good OBP don’t have a high SLG so it makes the
results iffy. There are better
stats to use but this is a good starting point. Another version of this stat that is starting to become a
little more popular is OPS+, pretty much it shows the percentage above or below
the average OPS a player is.
Example if a player is a 90 OPS+ he his 10% below average, 100 being the
baseline.
3)
HR/FB rate or Homerun to Fly Ball rate-
HR/FB. Pretty simple to explain,
basically its how often a player hits the ball in the air does it go for a
Homerun. It’s not a luck stat but
it is a stat that can act like one, if a player is hitting say a Homerun every
5 fly balls he’s hitting about a Homerun about 20% of the time which isn’t
sustainable and he will eventually average out.
4)
wOBA or weighted On-Base Average-
(0.691xuBB+.722xHBP+.884x1B+1.257x2B+1.593x3B+2.058xHR)/(AB+BB–IBB+SF+HBP). That’s a very large equation that
pretty much says not all players are equal. This is the catch-all offensive stat that says the value of
a hitter and takes all the 3 stats we call counting stats (AVG, OBP, SLG) and
combines them into one stat to show the value of the hitter. When I say that every player isn’t
equal it is more or less talking about how the 3 counting stats work which all
assume that the hitters are equal or the things they do are equal; a 2B is not
worth double a 1B. wOBA doesn’t
just take the 3 stats into account but every aspect of the hitter and
calculates for accuracy and scope more than numbers. The numbers used to calculate the stat changed on a yearly
basis.
5)
ISO or Isolated Power- ((2B) + (2x3B) +
(3xHR))/AB. Pretty much means
Extra Base Hits / At Bats; like I said in OPS there was a better stat to gauge
a hitters power numbers and here it is.
It only looks at when a hitter gets past a single. One thing about this stat is it needs
lots of data to make a good calculation, as in almost half a season before you
can start looking into it otherwise the sample size is just not small.
6)
Swing% or Swing Rate. A stat that is good on its own merits but more powerful when
combined with BB% K% and others to get a good idea at what the hitter is doing
when he swings. As the name
suggests it shows how often a batter is swinging, there are also stats to show
how often a hitter is swinging in the strike zone (Z-Swing%) and outside the
strike zone (O-Swing%). Using all
3 shows the tendencies of hitters and how often the pitcher can get them to
swing at pitches. A useful tool
for scouts and pitching coaches.
These are the main hitting stats
that I look when I look at a team or player (ISO is better for players then
teams). So lets take a look at
some of the pitching saber stats.
1) ERA
or Earned Runs Average- (ER/Innings Pitched)*9. The best way to look at this stat is pretty much what it
looks like, how many runs a player would give up in a 9-inning game. Or just the average of runs he would be
giving up in 9 innings overall.
Not a good stat to look for future success but it’s a nice way to just
look at a player and say, “oh ok that’s how he’s pitching so far.” For reference an average ERA is about
4.00.
2) WHIP
or Walks and Hits per Inning Pitched- BB+H/Innings Pitched. Much like ERA this stat is exactly as
it stats and shows how often a pitcher is letting a runner on base per inning
pitched, and much like ERA it’s not the best evaluation tool. Pitchers have very little control over
their BABIP (yes pitchers have one too) so the WHIP could be misleading for
good or ill. A average WHIP is
about 1.32.
3) Quality
Starts- No formula for this one just a simple counting stat and just an ok one
at that IMO. To get a Quality
Start the pitcher must go at least 6 innings and give up no more than 3 runs. The reason I don’t like this stat
simply comes down to that baseline, if a pitcher gives up 3 runs in 6 innings
he has an ERA of 4.50. Yes
technically he’s keeping the team in the game but that’s still
unimpressive. Also I should
mention that this stat was created by Scott Boras to help sell his players…not
I’m not kidding this stat was invented to get pitchers more money by making
them sound better. Not a terrible
stat but not my favorite to look at.
4) FIP
or Fielding Independent Pitching- ((13*HR)+(3*(BB+HBP))-(2*K))/IP + constant). This stat is what a pitchers ERA
should look like if the balls in play and timing are league average. To put it simply, it’s a stat that
makes the defense league average and see how much the defense he currently has
aids him and his ERA. I like this
stat since it shows a more accurate look at the pitcher at see if he’s getting
lucky or not. Example: if a
pitchers ERA is 2.30 and his FIP is 4.60 this means he’s getting very lucky and
that’s going to average out at some point, this works vise versa were a
pitchers ERA is 4.60 and his FIP is 2.30 meaning he’s unlucky and his defense
is hurting him a lot. An average
FIP should be around 4.00. There
is another stat called xFIP that calculates almost identically to this one but
uses HR/FB rate which I don’t like.
It tends to change too much since HR/FB rates can drop and rise to
inconsistently.
Well that’s most of the stats I
look at but not nearly all of them, but this is getting on in length so maybe
another day I’ll look into more of them and explain them as well. Also there are many stats that I
personally haven’t gotten the hang of yet so I want to learn them before I explain
them.