The following are screenshots from a file I created in Microsoft Excel. These are highly (read: not even a little bit, not even at all) advanced statistics. Do not try this at home.
First, I decided to analyze the advertisements Baylor Baseball sends out via twitter and instagram. Who's featured the most? Is there a correlation between their photo being featured and success on the field? So I took to the spreadsheets....
According to an in-depth analysis of the photos tweeted out by @BaylorBaseball, Duncan Wendel is a frequent flier in pre-game advertisements. Joe Kirkland, however, is more prolific in photos, garnering 3 different shots. Wendel is the only other player who garnered more than one shot for promotional purposes, but appeared once more than Kirkland. Sorry, everyone else. Both have 3 wins apiece when their photo is used prior to a game.
(Sorry about the "ONE" in the win column...Excel was being a sassmaster.)
Next, I decided to look at the post-game material. I tallied everyone who appears in every post-game score announcement this season, as well as the number of wins associated with the use of their visage. This is the result:
Finally, I decided to compile these two tables to determine the true "posterboy" for Baylor Baseball.
Based on the numbers, there's a TIE! Logan Brown and Duncan Wendel are both the most visible members of Baylor Baseball, at least according to pre and post-game advertisements. Both have been featured 9 times, appearing in 4 different photos. Logan is the King of the post-game score reports, and Duncan is the King of the pre-game promos. So there you go. Logan Brown and Duncan Wendel, most visible players of Baylor Baseball.
For some more legitimate stats, I decided to look at attendance and time of game.
The average time of a Baylor Baseball game is roughly 3 hours (time is listed in minutes), with standard deviations of close to 25 minutes. Attendance for all games, however, is quite skewed, ranging from crowds of 503 and 519 to 4589 during the LSU game of the Houston College Classic. Thus, the standard deviation is HUGE. Meanwhile, the attendance for home games is clustered much closer together, leading to a much smaller standard deviation.
Lastly, I looked at the number of wins/losses in each month.
In February, the Bears went 6-5, then went 6-11 in March, and fell to 4-8 in April. In sum, the team has a record of 16-24.
Yay for Excel and standard deviations and math and stuff! :)