Buck’s Bucks and the Unlabeled Spreadsheet

Tye Tolberman sat in his favorite armchair on the coldest day of mid-January in western Maryland, steam coming off his morning coffee as he looked out the window. He took one glance at the ice covering his driveway and decided he just wasn’t going outside today. On his side table sat a pile of unopened letters, a checklist of things needed to be done, and a printed-out spreadsheet a friend had given him the last time he went east to see the Bowie Baysox play last summer.

“It fell out of some scout’s binder the last time I was here. He went flying out of there after the starting pitcher got taken out, so I couldn’t catch him to give it back”, his friend told him as they sat and took in a night game. “Anyways, you can have it if you want. I’m not wasting my time with that fantasy baseball crap this year. I’ve got too much real life to live”, and he placed it in the hands of ol’ Tye Tolberman, manager of the going-on 14-year-old “Buck’s Bucks” fantasy team.

Filled with the slight tinge of excitement that only a print-out spreadsheet can bring, he scanned the rows. This was some advanced stuff. Labeled in the columns were BB%, K%, HR/FB, some really good stats. It was clearly displaying hitter stats. Given Tye and his friend were at that game in the late part of the summer, Tye had to assume he was looking at 2022 near-end-of-season stats. He could not, however, assume anything at all about who these stats were representing, there were no names. Maybe it was a scout’s way of keeping his data secret. Maybe it was garbage. Tye liked to think it was the fantasy gods, showering down secret data that only he had access to.

Neglecting all outdoor activities on his weekend chore list, Tye refilled his coffee cup, sunk a little snugger down into his winter slippers, and reached for the magical spreadsheet. In Tye’s fantasy baseball league, he was able to draft one player who had not yet made his major league debut. At the end of the season, he could choose to either keep that player and forfeit his minor league pick the following year or re-draft. Typically he and his league mates would draft players they knew were likely to get called up early in the season, players like Grayson Rodriguez, Bobby Miller, and Anthony Volpe. But the truth was that those players rarely contributed major value to teams in their first year and unless managers got lucky and kept a player who would later break out as a star, the pick was typically not a league winner. Tye decided he was going to have some fun with his minor league pick this year. He was going to make a pick from his spreadsheet. He started by pulling out every color highlighter he could find in his kitchen junk drawer, and began thinning out the herd by looking for players with a high PA count under the age of 26:

The Mystery Spreadsheet
Player Age PA SB CS AVG BB% K% BB/K SLG HR/FB SwStr%
15 22 138 1 0 0.356 13.0% 15.2% 0.86 0.678 17.8% 9.1%
22 23 254 7 2 0.231 13.4% 15.7% 0.85 0.330 6.0% 6.0%
2 22 122 4 2 0.303 16.4% 20.5% 0.80 0.475 6.9% 8.3%
17 24 495 24 5 0.282 12.7% 17.6% 0.72 0.419 6.4% 7.8%
7 25 53 0 1 0.167 9.4% 13.2% 0.71 0.188 0.0% 4.4%
13 21 126 1 0 0.286 13.5% 21.4% 0.63 0.476 13.5% 10.2%
19 25 443 21 1 0.220 14.4% 27.8% 0.52 0.429 16.7% 12.3%
9 22 26 0 0 0.043 11.5% 23.1% 0.50 0.043 0.0% 16.2%
20 23 316 5 3 0.263 13.6% 28.5% 0.48 0.429 8.3% 16.0%
21 22 136 3 1 0.207 8.8% 22.1% 0.40 0.379 10.8% 10.5%
11 25 235 1 2 0.225 10.6% 27.7% 0.38 0.343 6.7% 13.8%
12 22 424 10 6 0.247 7.8% 21.7% 0.36 0.389 5.7% 13.2%
1 23 57 1 0 0.353 8.8% 24.6% 0.36 0.686 20.0% 11.9%
16 24 412 9 2 0.281 9.2% 26.0% 0.36 0.486 14.0% 13.1%
18 24 159 10 2 0.277 9.4% 29.6% 0.32 0.489 16.2% 16.4%
6 25 443 11 2 0.239 9.0% 28.9% 0.31 0.499 22.0% 15.3%
23 24 480 18 1 0.219 7.3% 23.8% 0.31 0.333 4.0% 13.9%
3 21 521 28 6 0.259 5.6% 21.3% 0.26 0.387 6.7% 13.2%
14 25 12 0 0 0.000 8.3% 33.3% 0.25 0.000 0.0% 17.0%
8 25 82 1 0 0.147 8.5% 41.5% 0.21 0.191 0.0% 18.5%
4 23 51 2 0 0.130 7.8% 41.2% 0.19 0.283 10.0% 14.1%
24 24 157 5 4 0.255 3.2% 26.8% 0.12 0.409 8.2% 13.2%
5 24 42 0 0 0.171 2.4% 33.3% 0.07 0.341 18.2% 18.2%
0 27 7 0 0 0.143 0.0% 42.9% 0.00 0.286 0.0% 15.4%
10 25 4 0 0 0.000 0.0% 50.0% 0.00 0.000 0.0% 38.5%

Tye had always believed in the strategy of looking for players who walked more than they struck out, or at least, it sounded good. There were no players on this list that accomplished that impressive feat, but he could highlight a few players with good BB/K rates to find players who got close:

The Mystery Spreadsheet – Subset
Age PA SB CS AVG BB% K% BB/K SLG HR/FB SwStr%
22 23 254 7 2 0.231 13.4% 15.7% 0.85 0.330 6.0% 6.0%
17 24 495 24 5 0.282 12.7% 17.6% 0.72 0.419 6.4% 7.8%
19 25 443 21 1 0.220 14.4% 27.8% 0.52 0.429 16.7% 12.3%

Now he had three intriguing players. He liked the speed of players 17 and 19, but player 22 had a much lower K% with a lower SwStr% and higher BB/K rate to back it up. But, how could he ignore the 16.7% HR/FB rate and 0.429 slugging power of player 19? Regardless of who he picked, he needed to find these players names and positions. There must be some kind of way for him to identify who these players are, but how?

Can you help our friend Tye? Can you identify these three players? Place your guesses in the comments section!





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ChrisHurstmember
11 days ago

The list is from the Pirates AA team, the Altoona Curve. 17 is Jared Triolo, 19 is Andres Alvarez and 22 is Lolo Sanchez. Tye should take number 15, Endy Rodriguez. How the scout had final season numbers in the middle of summer is interesting. How I found the numbers is going to the leaders tab, pulling up all the minor leaguers, sorting by stolen bases (although plate appearances would have worked as well), and noticing the origin of the players.

ChrisHurstmember
10 days ago
Reply to  Lucas Kelly

Middle was the wrong word, but unless it was the last game of the season, knowing the final stats requires the ability to know the future.