SIS

@SportsInfo_SIS · Bethlehem, PA · joined 19 Oct 2016

Real-time, rich sports data for betting & media. Provides trusted analytics to ESPN, NFL, NBA, MLB, & Sportsbooks. ⚾️@sis_baseball 🏈@football_SIS 🏀@sis_hoops

22 895Followers

697Following

16 122Posts total

24.3KViews on collected posts

Postingan terbaru

SIS@SportsInfo_SIS13 Aug

This week on Off The Charts, our crew tried to provide explanations of football analytics that would serve as a good introduction to advanced stats. That includes both the pluses and minuses of Expected Points Added (via @VigManOnCampus) https://t.co/JJc8QMUKJS https://t.co/x

0:18

1.7K views · 3 likes · 3 reposts · 0 replies Open on X →

SIS@SportsInfo_SIS23 Jul

What does one put in a college football playoff predictor model? In the latest Off The Charts, @J_Weaver97 explains what went into his version. Lots for him and @VigManOnCampus to consider https://t.co/WZiDVJ3lG8 https://t.co/6SbGdDtqTJ

0:55

1.4K views · 5 likes · 4 reposts · 0 replies Open on X →

SIS@SportsInfo_SIS21 Jul

We collect a LOT of highly-specific football info at SIS and we've got some insights to share about it. @vigmanoncampus on our offensive line and pass rush detail data On predictable & unpredictable pass rushers, blocking analysis & more https://t.co/Dwc854FS66 1.4K views · 4 likes · 5 reposts · 0 replies Open on X →

SIS@SportsInfo_SIS23 Jun

NEW STUDY: We used our college basketball player data to fit players into different clusters and then analyzed which clusters have historically fared best in the NBA. 2 players who fall into favorable clusters: Zuby Ejiofor & Labaron Philon READ: https://t.co/Hnw9C1DRZW h

17.4K views · 48 likes · 12 reposts · 0 replies Open on X →

SIS@SportsInfo_SIS22 Jun

Drafting well is more important than ever for NBA teams hoping to compete for championships. @clee_data uses SIS data to determine which player archetypes offer the most consistent draft hit rates: https://t.co/Hnw9C1DRZW 2.5K views · 6 likes · 4 reposts · 0 replies Open on X →

Dibandingkan akun berukuran sama

3 postingan dari 90 hari terakhir, dibandingkan dengan rentang 10K–100K pengikut. tampil luas, tetapi sedikit penonton yang merespons.

Median tayangan1 403akun ini924median untuk 10K–100K

Jangkauan, %6.13%akun ini3.62%median untuk 10K–100K

Interaksi, %0.64%akun ini1.52%median untuk 10K–100K

Metrik	Akun ini	Median untuk 10K–100K	Rasio
Median tayangan per postingan	1 403	924	1.52×
Jangkauan (tayangan ÷ pengikut)	6.13%	3.62%	1.69×
Tingkat interaksi	0.64%	1.52%	0.42×

Akun lain pada rentang ini → Bandingkan dengan akun lain → Bagaimana tolok ukur ini disusun →

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

2.5K22 Jun

17.4K23 Jun

1.4K21 Jul

1.4K23 Jul

1.7K13 Aug

Last 5 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.40%22 Jun

0.35%23 Jun

0.64%21 Jul

0.64%23 Jul

0.36%13 Aug

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes57.4%66 in total

Reposts24.3%28 in total

Quotes0.9%1 in total

Bookmarks17.4%20 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.