Let’s start with a simple opinion that most college baseball followers will agree with: LSU’s offense is good. Like a cardiac patient getting shocked back into rhythm with a defibrillator, Dylan Crews and Tre’ Morgan’s insertion into LSU lineup to join Gavin Dugas and Cade Doughty reset the bats last season to the powerhouse lineups of old.
When Paul Mainieri retired at the season’s end, LSU hired Jay Johnson, the former head coach of an Arizona baseball team that led Division I with 537 runs and attained a coveted trip to Omaha with their offensive prowess. Then Johnson upped the ante further. He brought in star Arizona freshman third baseman Jacob Berry to Baton Rouge. LSU’s projected top five in the lineup will be a force.
But that’s a lot of words with little backing other than my word. Baseball is a beautiful game in that you can’t hide from the numbers. The statistics themselves will never lie, but rather stand witness to the consistent trial by fans probing who is genuinely good and who is not. The basic stats can tell you a simple enough story. Batting average, home runs, and more recently on-base percentage and slugging percentage are commonly used to quickly evaluate players.
Why should we stop there? Sometimes even those numbers don’t tell the whole story. Modern baseball tracking is fascinating, and advanced stats are the way of the future for all levels of the game. Major League Baseball has used sabermetrics, the statistical analysis of baseball performance founded by Bill James and built upon by other mathematical minds, to evaluate players.
The basis is very straightforward: get a smorgasbord of statistics down to one number or as close as possible to it. With the resources the MLB has at its disposal, the professionals use an all-important figure called WAR, or wins above replacement. Essentially, analysts take the number of runs a player contributes via his batting, baserunning, and fielding, adjusting it for his position and league, and convert it to the number of wins a player contributes by determining how many runs are needed to win a game.
Unfortunately, college baseball does not have the video tracking technology necessary to follow the baserunning or fielding runs that professional baseball does, nor is it standardized enough across conferences or seasons due to wide team disparities and rule changes for WAR to be accurately calculated.
However, that did not stop me from climbing the figurative sabermetric diving board and taking a plunge into some advanced hitting stats by obtaining available data from LSU, Arizona, and the SEC and plugging them into some relatively simple equations that enhance standard baseball statistics. Let’s define the numbers first with what it represents, the physical equation that holds it together, and how it helps enhance normal baseball analysis.
wOBA: Weighted On-Base Average
Think of wOBA to OBP as The Dark Knight to the original Batman. Sure, the 1989 Michael Keaton version works. It’s enjoyable, Jack Nicholson is a solid Joker, and it encompasses some of the good things that make the Caped Crusader the gritty, even-keeled hero of the streets. But Christian Bale’s Batman, Heath Ledger’s Joker and Aaron Eckhart’s Two-Face took everything to another level.
wOBA is a measure of a player’s time on-base while also taking into account that not every time a player gets on base results in the same outcome. OBP is effective for gazing past a player’s batting average as his single determinant for his ability to get on base and contribute runs, but OBP also looks at a walk, a double, and a home run as the same thing when they are not. wOBA uses weight factors to unbalance the six primary ways to get on base: walks, hit-by-pitches, singles, doubles, triples, and home runs, and then divides the sum of these products by plate appearances. In the MLB, these weight factors change on a year-by-year basis, so I used standardized values provided by Fangraphs.com to calculate for the five LSU hitters I analyzed: Crews, Morgan, Berry, Dugas, and Doughty.
Here’s the full equation if you want to try it yourself for another player:
((0.69 x BB) + (0.72 x HBP) + (0.89 x 1B) + (1.27 x 2B) + (1.62 x 3B) + (2.1 x HR))/(PA)
wRAA: Weighted Runs Above Average
wOBA is essential for many reasons, but perhaps its greatest utilization is in wRAA. Remember how I said earlier it’s all about getting everything down to one number as best as possible? The one number everybody wants to get to is wins. But it just so happens that in order to produce wins, you have to score runs. That is a trailblazing concept, I know.
We can use wOBA to translate it into the amount of runs a player contributes via his hitting, as he relates to the league average. To do this, I calculated the wOBA for the SEC last season and used that important reference to determine LSU players’ wRAA. I also used what is called a wOBA scale, an annual figure that changes relative to league performance in the pros. Since I had no ability to find out what that might be for the SEC, I used an on-average digit of 1.2 for the wOBA scale used in this equation. It may have some error, but not enough to where I don’t feel comfortable using it. Then, multiple by plate appearances and you’ve got the batting runs the player is adding. I feel this is the most direct way to analyze a player’s batting contributions. Because of how diverse wOBA’s makeup is, when founded in it, wRAA maintains that diversity, just in the form of runs instead of on-base average. When calculating WAR for MLB, analysts use this statistic plus league and position adjustments to create the most accurate batting runs number possible.
Here’s the equation:
((wOBA – league wOBA)/(1.2)) x (PA)
BABIP: Batting Average on Balls In Play
A good way to isolate a true batting average for a player from the influences of his power, walk, and strikeout rates is to use BABIP. This statistic just accounts for the player’s batting average on balls he put it into play, excluding home runs, walks, strikeouts, and hit-by-pitches. I like this one as a basic metric for a player’s contact rate and it might relate with his ability to hit for average without all the super cool, high-tech, big budget StatCast MLB gets. C’mon man. Invest in college baseball. I want barrel rates.
Here’s the equation:
(H-HR)/(AB-K-HR+SF)
ISO: Isolated Power
The yang to BABIP’s yin, ISO doesn’t care about little things. When BABIP cheers for you and tells you, “Great job, kiddo! Don’t worry about that effort, you’ll get ‘em next time!”, ISO jeers from the opposite dugout about how you didn’t stretch that single into a double or how that ball that scraped the right field wall turned out to just be a double and how weak and foolish you are for that. ISO cares about one thing and one thing only: power. It takes doubles, triples, and home runs, weighs each one accordingly, and divides them by at-bats. It’s been used for a very long time actually, as it was created by Brooklyn Dodgers executive Branch Rickey in the 1950s, well before Bill James started his writings. Personally, I think there is flaws in this as a singular stat to analyze because it fails to account for other things involved in being a power hitter, particularly strikeout rates.
The equation is very simple:
(2B + 2(3B) + 3(HR))/(AB)
PA/K: Plate Appearances per K
So, if ISO doesn’t account for strikeouts like I wanted it to, I needed to add another line to isolate that variable and examine it. There’s nothing complicated here, just the player’s number of plate appearances divided by his strikeouts. The result is the interval of plate appearances before a player strikes out on average. I think it’s important to look at ISO and PA/K together or else Joey Gallo would be a top 20 player in baseball with 38 home runs last year but a 2.892 PA/K hiding in the shadows. But also, if you hit more home runs, you’re going to strike out more. It just happens. Shrug.
Equation:
(PA)/(K)
Okay? Okay. Phew. Let’s get to the good stuff.

Let’s go stat by stat.
Holy wOBA. Crews and Berry last year were sensational. Bryce Harper led the MLB in wOBA last year with a .431. Granted, the MLB is a much more difficult league naturally, and I’m sure anyone who’s OPS was 1.116 (Crews) and 1.115 (Berry) last year would be doing just fine in these advanced metrics, but these guys are special. And look how well Dugas, Morgan and Doughty did as well. Their metrics make look less impressive when stacked next to the other two, but this is serious talent in all five spots. I think what’s more impressive is their comparison to the rest of the SEC. The conference averaged a .323 wOBA, which is perfectly normal. MLB’s average wOBA last season was .314. Crews and Berry topped the conference figure by near 70%.
When we got to wRAA, it was even more embellished. Crews and Berry each scored 36 runs at the plate last season. With just a quick look at MLB’s adjusted batting run leaders last season, once adjustments would be done, Crews and Berry would probably have ranked in the Top 10 percentile in the majors last season with the seasons they had. I think the power they displayed along with their ability to hit for average was something their teammates lacked a bit and explains the differences between them. I think especially in Tre’ Morgan’s case, for him to have as high a wRAA with just six home runs last season is a testament to how talented a contact hitter he is and will be this upcoming season.
And BABIP attested to that conclusion as well. Morgan’s .407 led everyone that was analyzed. Tim Anderson and Starling Marte led the bigs last season in BABIP with a .372 average each. Three of five LSU players here exceeded that. Dugas and Doughty lagged behind a bit but that could be due to their slightly higher totals in home runs (Dugas led the team with 19) and their slightly lower totals in singles. BABIP likes when the ball gets in play and hates when the ball leaves or never enters in the first place.
Meanwhile, ISO hates Tre’ Morgan and really loves Gavin Dugas. More home runs than anyone in 20-25 less at-bats than Crews and Berry will do that. And while Morgan hit 16 doubles, he only totaled 26 extra base hits last season. ISO just doesn’t get that players can be successful without hitting bombs. It’s okay ISO, you’re not alone out there. Fans love dingers more than anything too.
And sure enough, with great power, comes bad strikeout rates. I’m almost certain Uncle Ben said that to Peter Parker at least once before. Dugas’s 4.254 is a little low, but when he is as aggressive as he is at the plate with the pop in his bat he has, that’s a number I think both he and Johnson will be okay with. Look at Cade Doughty! Eight plate appearances without a K on average is awesome. Hopefully his plate discipline will continue to improve this year.
My hope is that this article will start up conversations about bringing advanced stats closer to a committed relationship with college baseball. The sport is growing faster than ever, and as a lifelong fan of the game, it warms my heart to see it getting so much attention from the professional baseball community. The tools the MLB has at its disposal can absolutely help the collegiate athletes grow their exposure and boost their draft stocks, and they would bring increased opportunities for fans of the college game who want to follow their teams more closely to engage in closer analysis.