Parsing Data in Google Sheets

I figured I would give a rundown how I parse data on Google Sheets (which can be translated to use in Microsoft Excel).

To start, I import data from an external site; it has to be in a table or .csv format. In this example, I'm importing from ESPN's schedule for the Tampa Bay Rays.

Projected ACC Standings

I've finished simulating every ACC team's season, and can now project the conference standings from those results.

Projected bowl teams: 10

Atlantic
1. Florida State   11-1 (7-1)
2. Clemson         8-4  (6-2)
3. Louisville      8-4  (5-3)
4. Boston College  6-6  (3-5)
5. Syracuse        5-7  (3-5)
6. NC State        6-6  (3-5)
7. Wake Forest     5-7  (2-6)

Coastal
1. Virginia Tech   8-4  (5-3)
2. Duke            7-5  (4-4)
3. North Carolina  7-5  (4-4)
4. Georgia Tech    7-5  (4-4)
5. Pitt            7-5  (4-4)
6. Miami (FL)      7-5  (4-4)
7. Virginia        4-8  (2-6)

Note: Notre Dame is not considered part of the ACC

Bonus (Alternative) UNC Season Predictions

The following was penned by "Stan Hooper"

ACC media days this week. (Quick immediate analogy. SEC media days are the trailer park meth. It'll have you up for days straight trying to figure out the madness going on there. It is the real college football stuff for real college football people. ACC media days is the methadone. Its the cheap imitation knockoff. It leaves the user in a zombie like state, however you appetite for meth is temporarily curbed until the next relapse and subsequent binge. ACC football turns you into a zombie, SEC makes you an addict because it is real.)​ Nothing interesting happened. But one thing did happen. UNC got picked fourth in the ACC coastal. 4th! 4th! Not 4th in the ACC, 4th in the Coastal. Behind Miami, behind VT, behind d00k. Gutless. Spineless. Brainless. They know not of what will happen this season. What kind of crackpots are voting in the ACC media poll?

Never mind what these bozos pick. 15-1 is what will happen. Before you try to go counting number of games and such, here is in great detail what will happen this year.

Simulating UNC's Season

I (once again) adapted my Monte Carlo simulator, this time to simulate the entire regular season of any given Division 1 FBS college football team. Its input was my roster-adjusted MDS preseason NCAAF rankings, and each season was simulated 10,000 times. Naturally, I've only done it so far for North Carolina.

An alternative, more optimistic preview can be found here.

North Carolina (Rank: #36)
Average record:      6.80-5.20 (4.27-3.73 in conference)
Most likely record:  7-5 (4-4)

Chance of making a bowl game (6-6 or better):  79.31%
Chance of going undefeated (12-0):             0.05%

Reconfiguring the MLB Standings by Removing Cluster Luck

As detailed in Grantland's most recent weekly MLB roundup, "cluster luck" has a big impact on how many runs a team scores/gives up, which then translates into wins and losses. Cluster luck is calculated by Grantland contributor Ed Feng, and described as follows:

Cluster luck stems from the idea that teams have little to no control over when hits occur, either the ones batters try to collect or the ones pitchers try to prevent. If a starting pitcher allows nine singles, but scatters them to avoid allowing a run, he’s lucky; and if a team manages eight hits in a game, but gets seven in one inning, it’s also lucky.

So, I determined the luck-independent MLB standings by first accounting for teams' "cluster luck" in their runs scored and runs allowed, and then calculating they're Pythagorean expected record from these adjusted runs scored/allowed (using an exponent of 1.83, which is the most accurate (and used by Baseball-Reference)).

Here are how the standings would look if luck was completely even across the board:

AL East	Wins	Losses	Win %	GB
TB	57	49	53.77%	-
TOR	56	51	52.34%	1.5
BAL	52	52	50.00%	4
NYY	49	56	46.67%	7.5
BOS	48	58	45.28%	9
AL Central	Wins	Losses	Win %	GB
DET	55	47	53.92%	-
CLE	52	53	49.52%	4.5
CWS	52	54	49.06%	5
KC	49	55	47.12%	7
MIN	46	58	44.23%	10
AL West	Wins	Losses	Win %	GB
OAK	65	40	61.90%	-
LAA	63	41	60.58%	1.5
SEA	55	50	52.38%	10
HOU	45	61	42.45%	20.5
TEX	40	66	37.74%	25.5
NL East	Wins	Losses	Win %	GB
WSH	57	46	55.34%	-
ATL	53	53	50.00%	5.5
NYM	51	55	48.11%	7.5
MIA	50	55	47.62%	8
PHI	46	60	43.40%	12.5
NL Central	Wins	Losses	Win %	GB
STL	55	49	52.88%	-
PIT	54	50	51.92%	1
MIL	55	52	51.40%	1.5
CIN	51	53	49.04%	4
CHC	50	54	48.08%	5
NL West	Wins	Losses	Win %	GB
LAD	60	46	56.60%	-
SF	54	51	51.43%	5.5
COL	49	56	46.67%	10.5
ARI	46	59	43.81%	13.5
SD	45	60	42.86%	14.5