Please welcome to the intermediate results for GW 36 with bonus points according to the BPS. Expected overall average is 16 points from 4 players. Average score in the top 10K is 24.1 points from 4.7 players (20.8 points net of hits).
SUMMARY
|
Random Sample |
Top 10K |
| Number of Managers |
20,000 |
10,000 |
| GW36 Average Score |
16.4 |
24.1 |
| Average Points Deducted for Point Hits |
0.9 |
3.3 |
| Players Played per Team (out of 12) |
4.1 |
4.7 |
| Captains Played |
16.7% |
33.8% |
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 |
7.6 |
46.3% |
8.7 |
36.1% |
| Goals Scored |
4.2 |
25.6% |
11.6 |
48.1% |
| Assists |
1.0 |
6.0% |
0.3 |
1.4% |
| Clean Sheet Points |
3.7 |
22.5% |
2.7 |
11.0% |
| Goals Conceded |
-1.0 |
-5.8% |
-1.4 |
-5.6% |
| Own Goals and Penalty Misses |
-0.8 |
-4.6% |
-1.5 |
-6.1% |
| Red and Yellow Cards |
-0.4 |
-2.1% |
-0.1 |
-0.4% |
| Saves and Penalty Saves |
0.2 |
1.5% |
0.2 |
0.8% |
| Bonus Points |
1.8 |
10.9% |
3.6 |
14.9% |
| TOTAL |
16.4 |
100.0% |
24.1 |
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 |
| Rooney |
13 |
2.8 |
17.2% |
|
Rooney |
13 |
9.7 |
40.3% |
| Mata |
13 |
1.9 |
11.9% |
|
Mata |
13 |
6.9 |
28.7% |
| Fonte |
6 |
1.1 |
6.6% |
|
Eriksen |
3 |
1.6 |
6.6% |
| Shaw |
7 |
1.0 |
6.2% |
|
Lallana |
3 |
0.9 |
3.9% |
| Lallana |
3 |
1.0 |
5.8% |
|
Shaw |
7 |
0.8 |
3.4% |
| Bony |
12 |
0.9 |
5.4% |
|
Fonte |
6 |
0.8 |
3.2% |
| Lambert |
5 |
0.6 |
3.8% |
|
Lukaku |
2 |
0.7 |
3.0% |
| Lukaku |
2 |
0.5 |
2.8% |
|
Boruc |
6 |
0.4 |
1.8% |
| Shelvey |
16 |
0.5 |
2.8% |
|
Bony |
12 |
0.4 |
1.7% |
| De Gea |
6 |
0.5 |
2.8% |
|
De Gea |
6 |
0.3 |
1.1% |
AVERAGE POINTS PER TEAM BY LINE IN FORMATION
|
Random Sample |
Top 10K |
| Line in Formation |
Points |
% of the Average |
Points |
% of the Average |
| Goalkeeper |
1.6 |
9.7% |
1.2 |
5.2% |
| Defenders |
3.9 |
24.0% |
1.8 |
7.5% |
| Midfielders |
5.2 |
31.5% |
9.8 |
40.6% |
| Forwards |
5.7 |
34.9% |
11.3 |
46.7% |
|
|
|
|
|
| Points for Captain |
2.9 |
18.0% |
8.7 |
35.9% |
| Points on the Bench |
4.2 |
25.7% |
3.0 |
12.4% |
|
|
|
|
|
| TOTAL |
16.4 |
100.0% |
24.1 |
100.0% |
AVERAGE POINTS BY POSITION
|
Random Sample |
Top 10K |
| Position |
Points |
Points |
| Goalkeeper |
4.4 |
3.8 |
| Defender |
2.6 |
1.1 |
| Midfielder |
3.7 |
6.3 |
| Forward |
5.9 |
7.8 |
| (*) This table only accounts for players who have played positive minutes this week |
GW 36 HALL OF FAME
GW 36 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 as of the start of the gameweek.
Guide to This Post
About This Blog
FPL Discovery 
I really enjoy these analysis pieces. Is there any chance you can include the top 1,000 (top 1k) too as I think the dynamics in there are fascinating.
Hey!
Well, I didn’t include these stats in my code to be automatically created because I thought they would be rather similar to the top 10K (usually that’s the case); and I didn’t want to scare people off with too many numbers. With only 2 gameweeks left, I’m not going to change my code, but I can create these stats additionally in about 5 minutes if/when people are interested. For example, here they are for this week (both stats and intermediate results): https://docs.google.com/spreadsheets/d/154slse0ofXD19S2ophQ2eUTTWYkc8gvbx0Kr7VX8oEg/edit?usp=sharing
Average is 26.6(-3.4) from 4.8 players so far 🙂
Thanks smartypants, top1k is super useful too 😀