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

Neueste Beiträge

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 →

Im Vergleich zu Konten gleicher Größe

3 Beiträge aus den letzten 90 Tagen, verglichen mit der Größenklasse 10K–100K Follower. wird weit gezeigt, aber nur wenige dieser Zuschauer reagieren.

Medianaufrufe1 403dieses Konto924Median für 10K–100K

Reichweite, %6.13%dieses Konto3.62%Median für 10K–100K

Interaktion, %0.64%dieses Konto1.52%Median für 10K–100K

Kennzahl	Dieses Konto	Median für 10K–100K	Verhältnis
Medianaufrufe pro Beitrag	1 403	924	1.52×
Reichweite (Aufrufe ÷ Follower)	6.13%	3.62%	1.69×
Interaktionsrate	0.64%	1.52%	0.42×

Weitere Konten dieser Größe → Mit einem anderen Konto vergleichen → Wie diese Vergleichswerte entstehen →

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.