Winds gusting, games called off, but FPL Discovery still welcomes you to the final results of GW 26. As usual, the results are with autosubs, vice-captains, and the following bonus points:
- Koscielny(3), Mertesacker(2), Sagna(1), Gibbs(1)
- Adebayor(3), Bentaleb(2), Paulinho(1)
- Crouch(3), Chico(2), Hernandez(1)
- Sturridge(3), Gerrard(2), Richardson(1)
An average FPL team was 3 players short this week, a top 10K had one player more. Expected average is 39 points; average score in the top 10K is 50.6 points.
SUMMARY
|
Random Sample |
Top 10K |
Number of Managers |
20,000 |
10,000 |
GW26 Average Score |
39.5 |
50.6 |
Average Points Deducted for Point Hits |
0.5 |
0.7 |
Players Played per Team (out of 12) |
9.1 |
10.1 |
Captains Played |
93.9% |
99.5% |
of Which: Vice-Captains |
14.3% |
3.6% |
GAMEWEEK RANK PROJECTIONS
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AVERAGE POINTS DISTRIBUTION
AVERAGE POINTS PER TEAM BY SOURCE OF POINTS
|
Random Sample |
Top 10K |
Source of Points |
Points |
% of the Average |
Points |
% of the Average |
Minutes Played |
17.7 |
44.7% |
19.9 |
39.3% |
Goals Scored |
5.9 |
14.9% |
8.1 |
15.9% |
Assists |
3.9 |
9.9% |
6.6 |
13.1% |
Clean Sheet Points |
8.9 |
22.6% |
10.2 |
20.1% |
Goals Conceded |
-0.6 |
-1.5% |
-0.5 |
-0.9% |
Own Goals and Penalty Misses |
-0.1 |
-0.1% |
-0.0 |
-0.0% |
Red and Yellow Cards |
-1.1 |
-2.8% |
-1.0 |
-1.8% |
Saves and Penalty Saves |
0.4 |
1.0% |
0.7 |
1.3% |
Bonus Points |
4.6 |
11.6% |
6.7 |
13.2% |
TOTAL |
39.5 |
100.0% |
50.6 |
100.0% |
10 PLAYERS CONTRIBUTING THE MOST TO THE AVERAGE
Random Sample |
|
Top 10K |
Player |
GW Points |
Per Average Team |
% of the Average |
|
Player |
GW Points |
Per Average Team |
% of the Average |
Sturridge |
15 |
4.8 |
12.3% |
|
Sturridge |
15 |
8.8 |
17.5% |
Fonte |
14 |
3.0 |
7.5% |
|
Adebayor |
16 |
4.3 |
8.5% |
Mertesacker |
8 |
2.4 |
6.0% |
|
Lallana |
6 |
3.8 |
7.4% |
Lallana |
6 |
2.0 |
5.1% |
|
Suárez |
2 |
3.7 |
7.4% |
Suárez |
2 |
1.9 |
4.9% |
|
Mertesacker |
8 |
3.2 |
6.3% |
Ivanovic |
10 |
1.6 |
4.2% |
|
Fonte |
14 |
2.8 |
5.5% |
Adebayor |
16 |
1.5 |
3.8% |
|
Koscielny |
9 |
2.3 |
4.5% |
Koscielny |
9 |
1.1 |
2.7% |
|
Hazard |
2 |
1.9 |
3.8% |
Gerrard |
11 |
1.1 |
2.7% |
|
Ivanovic |
10 |
1.8 |
3.5% |
Shaw |
5 |
0.9 |
2.3% |
|
Collins |
15 |
1.4 |
2.7% |
AVERAGE POINTS PER TEAM BY LINE IN FORMATION
|
Random Sample |
Top 10K |
Line in Formation |
Points |
% of the Average |
Points |
% of the Average |
Goalkeeper |
3.6 |
9.2% |
4.7 |
9.2% |
Defenders |
15.3 |
38.9% |
17.0 |
33.6% |
Midfielders |
9.2 |
23.2% |
10.4 |
20.6% |
Forwards |
11.4 |
28.8% |
18.5 |
36.6% |
|
|
|
|
|
Points for Captain |
6.2 |
15.6% |
5.2 |
10.3% |
Points on the Bench |
1.6 |
4.1% |
1.0 |
2.1% |
Points off the Bench |
5.4 |
13.8% |
5.8 |
11.4% |
TOTAL |
39.5 |
100.0% |
50.6 |
100.0% |
AVERAGE POINTS BY POSITION
|
Random Sample |
Top 10K |
Position |
Points |
Points |
Goalkeeper |
3.9 |
5.5 |
Defender |
6.2 |
6.7 |
Midfielder |
3.2 |
3.3 |
Forward |
4.2 |
6.2 |
(*) This table only accounts for players who have played positive minutes this week |
GW 26 HALL OF FAME
GW 26 HALL OF SHAME
About This Post
In this post, I take a look at 2 samples: randomly selected 20,000 FPL teams and the top 10,000 FPL teams. Because of the law of large numbers, we can make inferences about the overall FPL league based on the statistics for the random sample. Interval estimates for most statistics for the whole FPL game are also available. If you’re familiar with the concept of confidence intervals, you can point at a specific number characterising the random sample to see 99% confidence intervals for the respective number characterising all the 3 million FPL managers.
Love your website so much!
How could you do this excellent job! wow~!
Cheers, Carl 🙂
I’ve been visiting your site for a while now, makes for a great reading!
One question. Would it be possible to somehow find the rank distribution of FPL players still in the Fantasy cup?
Thanks!
Hey!
Sorry, but I don’t think it’s possible.
FPL don’t show any list of teams that are still in the cup until GW33. Right now there are 8,192 teams left in the cup, or 0.26% of all FPL teams. It means that if I do, say, my usual 20K random sample, there would be only around 50 teams that are still in the cup, which statistically would not be enough to make the sample representative. So, to get a more or less realistic rank distribution of FPL teams still in the Cup, I’d need to significantly extend the size of the sample, which would be just too time consuming and too much work for me.
Love this site! How do you do your data scrapping? What tools/languages? Interested in a squire?