While doing some work on my pre-season projections sheet, I came across a link to complete data from Razzball – complete full-season data for 48 12-team 5×5 fantasy baseball leagues. I’ve been using this as a handy cross-reference in doing some SPG (Standings Points Gained) calculations, but I decided to try and use the data to do an exercise on something I’d been thinking about: are some categories more important than others?
First, I looked at the by-category scores for all 48 first place teams, then all the second place teams, etc:
|1st pl teams||
|2nd pl teams||
|3rd pl teams||
|4th pl teams||
5th pl teams
The 48 first place teams, on average, scored 10.11 in the 5×5 categories. So basically a top-3 finish in all categories. Not that surprising.
Digging a bit deeper, I looked at the average score in each category for 1st place teams, then for 2nd place teams, and so on. I included the standard deviation (a measure of variability) and how often a team was in the top 3 for that category:
|1st Place teams||R||HR||RBI||SB||Avg||W||Sv||K||ERA||WHIP|
|% in top 3||77.1%||72.9%||70.8%||62.5%||41.7%||79.2%||75.0%||87.5%||64.6%||66.7%|
|2nd place teams||R||HR||RBI||SB||Avg||W||Sv||K||ERA||WHIP|
|% in top 3||58.3%||52.1%||68.8%||41.7%||43.8%||60.4%||68.8%||66.7%||62.5%||56.3%|
|3rd place teams||R||HR||RBI||SB||Avg||W||Sv||K||ERA||WHIP|
|% in top 3||54.2%||47.9%||54.2%||47.9%||33.3%||52.1%||50.0%||50.0%||39.6%||37.5%|
A quick glance seems to suggest that the most important categories were Runs on the batting side, and Ks on the pitching side: the average score for the team that won their league was highest – by quite a margin, and also varied less – for those two categories. Winning teams were also more likely to be at least in the top 3 in Runs and Ks compared to any of the other batting and pitching categories, respectively.
Conversely, Batting Average did not appear to be that important – less than half of the teams that won their league were in the top 3 in Batting Average, and it had the lowest average score for champion teams of all the 5×5 categories. It was also the most volatile – with a standard deviation of 2.9, around 67% of teams that won their league would have had a Batting Average score ranging from 11.2 down to as low as 5.3!
What about second-place teams? Ks and Runs were important here as well, but without the gaps seen for winning teams. The highest-scoring category on the pitching side was again Ks, but at 9.9, this was only 0.1 higher than the second category (Saves). On the hitting side, RBIs had the highest average score at 9.9, with Runs at 9.8
There’s another way to look at the data – if you were the leader in, say, Home Runs, how likely is it that you won your league? Here’s another breakdown:
|1st in category|
|% in top 3||75.0%||58.3%||56.3%||50.0%||31.3%||60.4%||58.3%||75.0%||60.4%||54.2%|
|2nd in category|
|% in top 3||39.6%||35.4%||56.3%||31.3%||31.3%||43.8%||41.7%||43.8%||27.1%||35.4%|
|3rd in category|
|% in top 3||20.8%||31.3%||25.0%||22.9%||22.9%||31.3%||43.8%||35.4%||39.6%||29.2%|
This table tells us, for example, that once again, teams that finished tops in Runs or K’s, had an average overall finish of 2.1 and 2.2, respectively: basically, they finished 1st or 2nd overall in their league, and fully 75% of teams that were first in Runs or K’s had a top-3 overall finish. (15 teams were first in both Runs and Ks – of those, 14 won the league; the lone exception came in third).
Conversely, teams that had the best Batting Average only finished 5th on average, and only 30% of teams with the best batting average were in the top 3.
I’m not showing the data here, but the reverse was also true: of the teams that were in the bottom half in the league in Runs, or in K’s, exactly none of them won the league. None. Only four teams (for both Runs and K’s) even managed a 2nd place overall finish!
On the flip side, there were 26 teams that were in the bottom half in Batting Average but 1st or 2nd overall, including 14 overall winners.
So the data appear to be telling us that we need to focus on Runs and Ks, and not worry quite as much about Batting Average. There may be some logic behind this: players scoring lots of runs are, perhaps, coming to bat more often, which means more opportunities for HRs, SBs and RBIs. Pitchers generating lots of Ks are perhaps more likely to be in position to pick up Wins and Saves and have better ratios.
While I don’t think anyone would recommend ignoring a category altogether – even Batting Average – I think the key takeaway is that in looking at roster construction, you might benefit by paying closer attention to Runs and K’s – for example, by letting those two categories be the tie-breaker if two players appear to be close in value.
Obviously, none of this is particularly new or revolutionary. And of course the usual caveats apply: 48 leagues from one particular year may or may not be a sufficient sample size to draw conclusions from. Results will almost certainly differ in some way or another for leagues with different settings (1 catcher leagues vs 2 catcher leagues, 5 outfielders & 1 util vs 3 OF and 2 util, etc). My knowledge (or lack thereof) of statistics and such could make the entire exercise completely worthless, etc.
But I, at least, found it interesting – that’s all that matters, really – and I am looking to incorporate this as I do my projections this year.
 12-team, standard 5×5, 5 outfielders and one utility spot; max 180 games started for pitchers, and – at least according to Razzball – the Razzball leagues are supposed to be generally more competitive that more casual leagues.