- Joined
- Feb 23, 2018

- Messages
- 485

**What is BaseRuns?**

BaseRuns is a metric that tries to remove “sequencing” from the equation to determine overall strength of a club. Sequencing is simply the order in which events occur and have shown to be quite random in nature. For instance, in a one game sample a team who has two hits and one walk can beat a team with nine hits and five walks if the sequencing allows for it. I. E. a walk, hit, then HR would create three runs for Team A, and Team B may have the more hits and walks, but if the sequencing is spread out over the course of the game they may not score any runs at all. Over the course of a season, these events should even out and create a more realistic impression of what a team should be in quality. If you are into more advanced stats this is metric is sometimes called “Second Order Wins.”

The difference between Miami’s “Expected winning percentage” and their actual winning percentage was fairly large at 0.068 (.587 - .519). In the ACC, the only clubs who had a larger gap between their expected winning percentage and their actual winning percentage were Boston College and Wake Forest, who tied for the largest gap at 0.086. Clemson was the most fortunate club in the ACC, outproducing their expected winning percentage by -0.070 (0.763 to an expected 0.693).

**Projections:**

Running projections for college players is a less refined exercise than what you’d see at the major league level. Smaller sample sizes, more unequal quality of competition, and park factors all can impact the results of players at the college level that are less impactful at the professional level. I’ve regressed hitting projections towards the mean in Batting Average on Balls In Play (BABIP), BB%, K%, HR%. For pitching, I’ve regressed projections towards the mean in BB%, K%, HR%, BABIP, HBP%.

The main hitting metric is Weighted On-Base Average (wOBA) which attachs linear weights to each offensive outcome and attempts to weight the outcomes into a number that is on the same scale as On-Base Percentage for the league.

The main pitching metric is Fielding Independent Pitching (FIP) which removes defensive ability from things the pitcher is directly responsible for (walks, strikeouts, hit-by-pitch, HR’s). Once a pitch is released, the pitcher no longer has any control on the outcome of the at-bat, it is up to the defense. This number is scaled to match the league average ERA.

**Freddy Zamora-**210 AB’s, .319/.402/.438, .344 BABIP, 10.5% BB, 8.9% K,

**0.365 wOBA**

Tony Jenkins-199 AB’s, .256/.398/.377, .322 BABIP, 17.1% BB, 19.9% K,

Tony Jenkins-

**0.348 wOBA**

Willy Escala-196 AB’s, .281/.376/.372, .380 BABIP, 10.9% BB, 24.3% K,

Willy Escala-

**0.332 wOBA**

Dylan Cloonan-193 AB’s, .269/.388/.404, .360 BABIP, 15.4% BB, 23.5% K,

Dylan Cloonan-

**0.351 wOBA**

Michael Amditis-145 AB’s, .269/.329/.331, .328 BABIP, 7.4% BB, 18.4% K,

Michael Amditis-

**0.291 wOBA**

Adrian Del Castillo-124 AB’s, .269/.371/.427, .340 BABIP, 10.5% BB, 19.6% K,

Adrian Del Castillo-

**0.343 wOBA**

Anthony Vilar-123 AB’s, .236/.309/.301, .315 BABIP, 7.9% BB, 22.3% K, 0

Anthony Vilar-

**.274 wOBA**

Chad Crosbie-111 AB’s, .270/.383/.369, .345 BABIP, 14.2% BB, 20.1% K,

Chad Crosbie-

**0.336 wOBA**

Chet Moore-110 AB’s, .273/.385/.327, .357 BABIP, 12.8% BB, 19.5% K,

Chet Moore-

**0.325 wOBA**

Alex Toral-102 AB’s, .225/.379/.382, .308 BABIP, 17.4% BB, 28% K,

Alex Toral-

**0.338 wOBA**

Isaac Quinones-98 AB’s, .265/.348/.357, .333 BABIP, 8.9% BB, 20.5% K,

Isaac Quinones-

**0.313 wOBA**

Jordan Lala-93 AB’s, .258/.352/.323, .333 BABIP, 10.9% BB, 20% K,

Jordan Lala-

**0.304 wOBA**

Ray Gil-79 AB’s, .263/.348/.418, .340 BABIP, 10.5% BB, 26.3% K,

Ray Gil-

**0.333 wOBA**

JP Gates-76 AB’s, .263/.348/.408, .305 BABIP, 11.2% BB, 19.1% K,

JP Gates-

**0.327 wOBA**

Gabe Rivera-67 AB’s, .254/.338/.448, .311 BABIP, 9.1%, 26% K,

Gabe Rivera-

**0.334 wOBA**

Pitchers

Evan McKendry-96 IP, 3.47 ERA, 2.80 FIP, 31.2% K, 8.1% BB, 23.1% K-BB

Pitchers

Evan McKendry-

**Chris McMahon-**80 IP, 3.94 ERA, 3.61 FIP, 20.7% K, 7.5% BB, 13.2% K-BB

**Greg Veliz-**69 IP, 3.39 ERA, 3.50 FIP, 25.3% K, 12.5% BB, 12.8% K-BB

**Brian Van Belle-**56 IP, 4.47 ERA, 4.59 FIP, 16.9% K, 9.3% BB, 7.6% K-BB

**Daniel Federman-**44 IP, 2.86 ERA, 3.04 FIP, 24.5% K, 8.2% BB, 16.3% K-BB

**Slade Cecconi-**41 IP, 3.73 ERA, 3.77 FIP, 23.5% K, 11.8%, 11.8% K-BB

**JP Gates-**39 IP, 3.00 ERA, 3.65 FIP, 24.4% K, 10.7% K, 13.7% K-BB

**Mark Mixon-**23 IP, 3.47 ERA, 3.56 FIP, 31.4% K, 10.5% BB, 20.9% K-BB

**Tyler Keysor-**22 IP, 3.27 ERA, 3.93 FIP, 23.6% K, 12.4% BB, 11.2% K-BB

**Jeremy Cook-**13IP, 4.85 ERA, 6.01 FIP, 25% K, 21.4% BB, 3.6% K-BB

**Albert Maury-**13 IP, 4.85 ERA, 4.94 FIP, 17.6% K, 11.8% BB, 5.9% K-BB

**Daniel Rivero-**10 IP, 6.30 ERA, 6.47 FIP, 16.7% K, 18.8% BB, -2.1% K-BB

**Overall Projections**

I know many of you skip to the end, so I’ll wrap it up from a team perspective here. Personal numbers are all regressed to account for several different outcomes, but the total team numbers will be fairly similar to what we will end the year with from a quantity standpoint.

Overall, I have Miami hitting .268/.370/.378 as a team with a 0.331 wOBA. For comparison, last year Miami hit .257/.356/.358 with a 0.317 wOBA so I do have them improving quite a bit. The average team in the ACC last year hit .268/.372/.406 for a 0.340 wOBA. When you factor in park factors for many of the clubs hitting in better hitting parks for their home games, I have Miami projected as a roughly average offensive club this year.

On the pitching side I have Miami being a standout with a team ERA of 3.71, a 3.71 FIP, 24.1% K, 10.5% BB, 13.6% K-BB. For comparison, last year Miami was at 3.74 ERA, 3.83 FIP, 23% K, 14.4% BB, 8.6% K-BB. In the ACC, the average team had an ERA of 4.13, a 4.13 FIP, 21% K, 16.5% BB, 4.6% K-BB rate.

Using the two metrics together, you can compile an expected winning percentage using BaseRuns. Miami checks in with a winning percentage of .647, which means I am projecting a record of 37-19.

Because I’m a madman, I built an RPI calculator and input the schedule for this year into the calculator. While it uses last year’s RPI numbers for the opponents, generally enough opponents will improve/decline at a similar rate that the numbers are a nice proxy for the next season. Plugging in 37 wins for Miami in a way that makes sense given who/where they play teams, you get to a projected RPI of .5719. Last year, that RPI had this cluster of teams around that number (Jacksonville, NC State, Coastal Carolina, Missouri State) and would’ve been number 24. NC State and Coastal Carolina hosted Regionals, while Jacksonville was a two-seed in Gainesville and Missouri State was a three-seed in the Ole Miss Regional.

This projection will clearly get Miami back into the NCAA tournament and put them squarely on the hosting bubble. I look forward to an excellent season and getting Miami baseball back to the tournament where it belongs.