FanPost

What it means to lead the league in April...in OTM league fantasy baseball!

I was inspired by our fearless leaders at OTM to analyze if a hot start to the season spelled good things...for our fantasy teams! First of all, a few disclaimers: I only have last season's worth of data to pull from, so the sample size is small, though over our three leagues we have an n that's better than 30 (48, 16 players x 3 leagues), so it is actually not terrible to begin with. More importantly, I haven't worked with stats in a couple years and I only have access to basic online tools since I don't have access to SPSS at the moment. So please, if you have expertise, please tell me if and when I make a critical error in this stuff. For the moment I'm just sticking to very basic linear regression analysis based on my limited knowledge and tools.

So my basic question is: how significant is April to gauging a team's success? To find out, I looked at the standings and points in April in all three leagues, then compared them to the final season standings and points.

Here is my dataset:

End of April End of Season

OTM 3

Player Wins Points
Player Wins Points
Pwangsta 4 1648
Pwangsta 16 7169.5
WCS 4 1374
WCS 16 7069.5
Fried Chick 3 1368
Bash Dash 16 6333.5
SG Sox 3 1310
Abdab 16 6755.5
Pesky 2 1325
R9 14 6723.5
Bash Dash 2 1317.5
Fried Chick 12 6754.5
Vipers 2 1315
Vipers 11 6473
R9 2 1263
Pesky 11 6110.5
OoLF 2 1263
Lackbar 10 5929.5
Abdab 2 1236.5
SG Sox 9 5746.5
Boobs 1 1187.5
OoLF 9 5473
Lackbar 1 1176
Aloha 8 5829.5
Aloha 1 1148
Boobs 6 5441.5
Jersey 1 1107.5
flasox 6 4989.5
Kraken 1 1017
Kraken 5 5255
flasox 0 812.5
Jersey 3 4700

OTM 2

Player Wins Points
Player Wins Points
ADP LP 4 1405
TLS 19 7792
TLS 3 1549
Franchise 17 6799.5
As Silver 3 1467
Silver 15 6950
Franchise 3 1306
ADP LP 14 6291
Box 2 1393
Mustache 13 6025
Sioux 2 1319.5
Box 11 6050.5
League 2 1304
LFO 11 5982
Seattle 2 1237
Seattle 10 6049.5
Heeb-Geeb 2 1218.5
WAR 10 5912.5
Mustache 2 1183.5
Sioux 9 6020
Rick 2 921
Rick 8 4830.5
Bad Guys 2 873.5
Heeb-Geeb 7 5835
WAR 1 1095
League 7 5516.5
Joes 1 1092
Bad Guys 6 5657
LFO 1 1064.5
Joes 6 5305.5
Orphans 0 956
Orphans 5 5055

OTM 1

Player Wins Points
Player Wins Points
BZ 3 1390.5
Ducks 16 6466.5
Sandy 3 1384.5
Chuck 15 7102
dsharp 3 1381.5
Sandy 15 6616
LC 3 1374
Brogshan 14 6687
Solo 3 1276
Solo 14 6670.5
NbN 3 1258
BZ 14 6653
SCG 3 1224
SCG 14 6178
Chuck 2 1422.5
NbN 12 6270.5
Brogshan 2 1351
Wolf 10 6647
Wolf 2 1320
R9 10 5604
Revived 2 1294.5
LC 9 5800
BnB 2 1096
WCS 7 5784
WCS 1 1272
Revived 6 6175.5
Bloggy 0 1160.5
Bloggy 6 5574.5
R9 0 1019
BnB 4 5184.5
Aloha 0 982
Aloha 2

4992.5

First, I ran a basic linear regression to find out how correlated points are to wins in the final standings. I came up with an r of .7975. Pretty good! This means that points and wins are highly correlated - not a big surprise, though having the most points does NOT mean you are guaranteed to have the most wins by any stretch, as a look at the dataset can tell you.

This can be shown by the r^2 of .636. An r^2 of .636 is so-so. It means that the points dataset explain about 64% of the variation in the wins dataset. Not too surprising - some other variables we might consider in the future might be variables such as points against or some sort of "pitching blowups" factor, for example.

Now let's do the same analysis with the April dataset. The predictability drops across the board: an r of .7442 indicates that points are still highly correlated with wins, but the r^2 is down to .554 which shows a drop in the ability of points to explain the wins dataset. As it is still early, this is an expected outcome, and I am sure you can pick out some outliers across the dataset presented above for points not matching up with wins.

But let's look at if April helps explain the final standings and points. First let's see if Wins in April explain final win totals. Wins in April give us an r of .7394, indicating a fairly high degree of correlation. I was a little surprised to see this, as I expected it to drop more. But it does appear that if you do well in April you are fairly likely to carry on that high win total into September. Our r^2 is .547, showing that only about 55% of the variation in final win totals is explained by the April win totals.

Second to last, we'll look at April points and if they correlate to final points. This is actually a very interesting result. I expected April points to not correlate well to final points at all. However, they blow away the win analysis with an r of .8006 and an r^2 of .641, showing that April points are highly correlated with final points AND that they explain over 64% of the variation in the final points dataset. 64.1% is still not fantastic, but it's better than even the points vs. wins analysis, showing that what you score in April might just be a decent indicator of your team's overall points scoring ability. Of course there are still other factors to consider, like injuries or underperformance, and just ask Rogue Nine if a bad April can bury an otherwise high scoring team.

Finally, let's look at April points and final wins. Do the points you score in April help to predict your final win total? This could be valuable to showing what you should do if your team is clunking along in April. It ends up being more helpful than April wins. The r is .761 and the r^2 is .579, showing a high degree of correlation but a moderate r^2. April points are still a useful tool in general that help explain your final win total, but exercise skepticism.

This study is clearly weakened by the lack of multivariate regression analysis, which I just don't have the tools to pull off right now, unfortunately. I would expect the r's and r^2's to drop across the board when exposed to alternate causation possibilities. Still, given even this limited data set, the degree of correlation between April wins and/or point totals and final standings is much higher than I anticipated.

In conclusion, I would examine your April with a specific regard to your team (taking into account injuries and underperformance), but also keep these results in mind when deciding if you can get back into it, or if it's time for a retooling.

P.S. I have the raw datasets on file if anyone is interested in pursuing this further.

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