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Spencer Mossman ✓

@fc_mossman · Florida, USA · joined 13 Jan 2017

trying to apply data to football intelligently; @beyond90ftbl; DM for inquiries

25 757Followers
953Following
10 128Posts total
542.3KViews on collected posts

โพสต์ล่าสุด

Just a guess but I would assume David Raya has also faced a higher percentage of shots from outside the box than most keepers, because Arsenal has such a strong defense. So I think this says something very different than what it’s trying to. 55.9K views · 1K likes · 66 reposts · 12 replies Open on X →
Rudder x engine remains my favorite pivot combo. Rudder: positionally intelligent oop, recycler and progressor Engine: box to box runner, carrier, up and down the pitch active everywhere Chema x Ayari is a textbook example of this… balanced and complimentary. 6.3K views · 95 likes · 5 reposts · 3 replies Open on X →
Big 24 hours for team #data 7.3K views · 63 likes · 0 reposts · 3 replies Open on X →
Mikkel Damsgaard as Iraola’s 10 would go quadruple platinum 25.1K views · 365 likes · 9 reposts · 10 replies Open on X →
@fc_mossman one of the best pieces i’ve read and this a great thing you’ve made. can see this becoming a big thing later on. great stuff 411 views · 1 likes · 0 reposts · 0 replies Open on X →
@fc_mossman I really liked it a lot, the intuition behind it is what striked me. I liked the VP model as well. 1.5K views · 3 likes · 0 reposts · 1 replies Open on X →
@fc_mossman It's a model that interested me. It considers the average position of a player and then infers the value of each action in terms of eventing. It is halfway between a rating and xT/EPV but considering only FBref data, pure eventing, without coordinates or tracking. 2.1K views · 6 likes · 0 reposts · 1 replies Open on X →
My new player Action Impact Model (AIM), explained. If you have an interest in football analytics, I encourage you to give this a read and share any thoughts or feedback for improvements. 🤝 The Action Impact Model https://t.co/NQC1XozJZ7 443.7K views · 350 likes · 23 reposts · 14 replies Open on X →

เทียบกับบัญชีขนาดเดียวกัน

4 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 10K–100K ผู้ติดตาม แสดงต่อคนมากกว่าบัญชีขนาดเดียวกัน.

ยอดดูมัธยฐาน16 162บัญชีนี้1 018ค่ามัธยฐานของ 10K–100K
การเข้าถึง, %62.75%บัญชีนี้3.99%ค่ามัธยฐานของ 10K–100K
การมีส่วนร่วม, %1.61%บัญชีนี้1.55%ค่ามัธยฐานของ 10K–100K
ตัวชี้วัดบัญชีนี้ค่ามัธยฐานของ 10K–100Kอัตราส่วน
ยอดดูมัธยฐานต่อโพสต์16 1621 01815.9×
การเข้าถึง (ยอดดู ÷ ผู้ติดตาม)62.75%3.99%15.7×
อัตราการมีส่วนร่วม1.61%1.55%1.04×

บัญชีอื่นในช่วงนี้ →   เปรียบเทียบกับบัญชีอื่น →   ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

443.7K31 Oct
2.1K1 Nov
1.5K22 Jan
41120 Aug
25.1K18 Sep
7.3K19 Sep
6.3K
55.9K20 Sep

Last 8 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.09%31 Oct
0.33%1 Nov
0.26%22 Jan
0.24%20 Aug
1.56%18 Sep
0.91%19 Sep
1.66%
1.97%20 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.55%.

What the audience does

Likes76.3%1 898 in total
Reposts4.1%103 in total
Replies1.8%44 in total
Quotes0.8%19 in total
Bookmarks17.0%422 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

บัญชีที่คล้ายกัน