Top of the 49th: Advanced stats don’t lie

BY MIKE CORMACK – sportsnet.ca

Last week, we extended an olive branch to our friends in the sabermetric community with our open letter to stats geeks.

This week, we thought we’d nerd-it-up a notch ourselves by digging into four random advanced stats while putting a Blue Jays spin on them to hopefully make them easier for you (OK, us) to understand.

The results? Besides a headache, a whole new appreciation for Rance Mulliniks. On to the geekery…

Stat: WAR (Wins Above Replacement)

Purpose: To show how many more wins a player would give a team as opposed to a “replacement level,” or minor league/bench player at that position.

Fangraphs.com formula: An abacus, Ouija board and some incense are required. Just kidding…well, sort of. If you want all the mind-numbing details, click on the link above but basically, position players are evaluated using statistics for fielding and hitting, while pitchers are evaluated using statistics related to the opposing batters’ hits, walks and strikeouts.

WAR In Blue Jays-ese: Most regular position players, say Lyle Overbay (2.4 in 2010), will accumulate a WAR of 1-3 over a season. Jose Bautista led the Jays in 2010 with at 5.7 while Tamp Bay’s Evan Longoria led MLB at 7.4.

Highest single season WAR in Jays history:

1. Roger Clemens 10. 5 (1997)

2. Pat Hentgen 8.4 (1996)

3. John Olerud 8.2 (1993)

4. Dave Stieb 7.7 (1984)

5. Roger Clemens 7.5 (1998)

Stat: BABIP (Batting Average On Balls In Play)

Purpose: Measures how many of a batter’s balls in play go for hits, while trying to take defence and luck into the equation. Fantasy lovers and GMs love this stat because (they say) it has great predictive value. BAPIP is also used for pitchers, but save that topic for a future blog.

Fangraphs.com formula: Hits – Homers / At-bats – Ks – homers + Sac Flies.

BABIP In Blue Jays-ese: The league average BABIP for a hitter is between .290 and .310, so if a player’s BABIP is unusually high (Corey Patterson) or too low (John McDonald), it’s usually a good indication of whether they are due to enter into a slump, or start seeing a few more balls falling in for hits. See below and panic accordingly.

Random 2011 Blue Jays betters’ BABIP (as of May 24):

Corey Patterson: .325

Jose Bautista .309

Aaron Hill .291

John McDonald .228

Juan Rivera .244

Stat: FIP (Fielding Independent Pitching)

Purpose: It looks like ERA, it smells like ERA, but it is not ERA. According to sabermetrics lore, a wise sabermetrician by the name of Voros McCracken (no relation to Ernie) decided that since pitchers have little control over balls in play, a better way to assess their talent is by looking at things they can control such as strikeouts, walks, hit by pitches, and home runs.

Or, as Big League Stew explains it: “Put another way, it tells you how well he should have done, not how well he actually did.”

So whereas ERA is an indication of how a pitcher fared, FIP attempts to provide an indication of how a pitcher will perform in the future (pay heed fantasy lovers).

Fangraphs.com formula: (HR x 13 + (BB + HBP – IBB) x 3 – K x 2) / IP, plus a league-specific factor (usually around 3.2). Got that?

FIP in Blue Jays-ese:

Jays starting pitchers ERA/FIP from 2010:

Ricky Romero 3.73/3.64

Brandon Morrow 4.49/3.16

Brett Cecil 4.22/4.03

Shaun Marcum 3.64/.374

So what are we to conclude from this?

Well, based on the definition above, Morrow’s 2010 ERA did not properly reflect his performance last season, likely as a result of some poor luck (and defence) behind him. As a result, (with any luck 😉 Morrow’s 2011 ERA should be considerably lower.

Conversely, it appears the other four starters weren’t as unlucky as Morrow and their ERAs were an accurate reflection of their performance.

But enough of me. I’m sure this cartoon can do a better job of explaining FIP…

Stat: WPA (Win Probability Added)

Purpose: To measure how much a player contributed to the team’s likelihood of winning. So say Fangraphs: “WPA takes into account the importance of each situation in the game. A walkoff home run is going to be weighted more then a home run in a game that has already gotten out of hand.”

Sounds fabulous in theory….

Fangraphs.com formula : They use something called a “win expectancy chart” which you can bore yourself with here. After taking a peek at it and deciding we’d have more fun filling out our Canadian census form, we’ll just have to trust their math on this one.

WPA in Blue Jays-ese: OK, so we look up the Blue Jays single-season leaders in WPA on baseball-reference.com and it totally jives with how we might have imagined this list playing out in our head:

1. Carlos Delgado 6.6 (2000)

2. Carlos Delgado 6.6 (2003)

3. John Olerud 6.1 (1993)

4. Paul Molitor 5.2 (1993)

5. Roberto Alomar 4.6 (1992)

Alas, then our eyes veered to the right on the page at the Jays career leaders in WPA:

1. Carlos Delgado 28.8 – Seems logical.

2. Roberto Alomar 12.7 – Boy, as good as Robbie was, Delgado sure was awesome.

3. John Olerud 9.8 – Really? He must have really made them count in ’93.

4. Fred McGriff 9.1 – One of the most underappreciated Jays ever.

5. Rance Mulliniks 8.1 – I just fell out of my chair.

Most of us remember Rance for his majestic ginger ‘stache and huge ‘80s frames, but do we also remember how valuable a player he was in a mostly platoon role at third base from ’83 through ’88?

We sure didn’t.

During that six-year run Mulliniks averaged a .294 batting average with 10 homers and 48 RBIs. Nice numbers, but nothing that was going to jump out at you on the back of your
’88 Donruss card.

But as a result of the platoon with first Garth Iorg, and later Kelly Gruber, Rance averaged just 348 at-bats in those six seasons.

Now: here’s what he averaged over those same six years extrapolated over a standard full season of 600 at-bats: .294/.374/.832 (batting average/OBP/OPS) 16 home runs and 82 RBIs, or just shy of all-star numbers.

And therein lies the real beauty of learning about WPA (and advanced stats in general): For if not for Rance’s eyebrow-raising placement on that career WPA list, we might never have taken the time to properly evaluate his numbers in greater depth, and as a result, we now have a better appreciation of what his true value was to some of those great Jays teams of the mid ‘80s.

As mentioned earlier, next week we’ll break down and translate a few more advanced stats, this time with a special cynical eye towards defensive metrics such as UZR (ultimate zone rating) .

Can’t wait to see what we find on the likes of Vernon and poor Edwin.

In the meantime, if there’s any other particular stat you’d like us to focus on, holla below and we’ll do our best to include them.

NOTE: An iPhone calculator was used in the making of this report.

You can also follow us on Twitter: @mikecormack