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Sam Hoppen ✓

@SamHoppen · Chicago, IL · joined 20 Aug 2011

NFL Data Scientist at theScore Bet | If I’m not making charts, I’m traveling or drinking beer | Opinions are my own

24 003Followers
792Following
33 966Posts total
195.2KViews on collected posts

Neueste Beiträge

Yeah, red zone efficiency was insane this week https://t.co/a8EzDZm2NJ
9.9K views · 33 likes · 4 reposts · 0 replies Open on X →
2026's first edition of Hoppen to Conclusions is LIVE! I have 29 charts to peruse as well as commentary on: 🤠 If it's time to worry about the Texans' defense 🐏 The Rams' low pass rate 🧀 Offensive line woes for the Packers 🦁 Penei Sewell's first game at LT ➕ And MORE 🔗⤵️ https://
2.1K views · 3 likes · 1 reposts · 1 replies Open on X →
Was asked thoughts on why week 1 scoring was up so much vs prior week 1s and just in general. Did a little digging and the one metric that stood out above the rest by far was just the red zone TD conversion rate. Offenses converted red zone opps into TDs at a ridiculous 67% 20.6K views · 63 likes · 2 reposts · 5 replies Open on X →
https://t.co/bTe4aysKhn
3.4K views · 3 likes · 0 reposts · 0 replies Open on X →
Will be out this evening! We've got 29 different charts coming to your inbox shortly! 3.2K views · 5 likes · 0 reposts · 1 replies Open on X →
@SamHoppen Me eagerly awaiting the Hoppen to Conclusions write-up https://t.co/9Mocad0Bjn
GIF
3K views · 0 likes · 0 reposts · 0 replies Open on X →
@SamHoppen This is GREAT and p much confirms all of my suspicions. It’s tenuous to use ybc to measure oline play because like you said it’s very nebulous. I’m wondering how 10 yard split would perform in the model. Feels like short area acceleration would be pivotal in creating y 984 views · 6 likes · 0 reposts · 1 replies Open on X →
Link to read: https://t.co/XifMmFoVxY Thanks to my friends at @SumerSports for the data that allowed me to do this research! 7.8K views · 25 likes · 4 reposts · 0 replies Open on X →
Is yards before contact an offensive line stat? Less than we treat it like one. I took a deep dive on a stat that we may have been reading wrong, using a couple of different modeling techniques to explain why. You can read it FREE on my Substack 🔗⤵️ https://t.co/gNoaa4PbLC
144.3K views · 234 likes · 40 reposts · 22 replies Open on X →

Im Vergleich zu Konten gleicher Größe

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

Medianaufrufe3 362dieses Konto924Median für 10K–100K
Reichweite, %14.01%dieses Konto3.62%Median für 10K–100K
Interaktion, %0.23%dieses Konto1.52%Median für 10K–100K
KennzahlDieses KontoMedian für 10K–100KVerhältnis
Medianaufrufe pro Beitrag3 3629243.64×
Reichweite (Aufrufe ÷ Follower)14.01%3.62%3.87×
Interaktionsrate0.23%1.52%0.15×

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

144.3K29 Jul
7.8K
984
3K16 Sep
3.2K
3.4K
20.6K
2.1K17 Sep
9.9K

Last 9 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.21%29 Jul
0.39%
0.71%
0.03%16 Sep
0.19%
0.09%
0.35%
0.23%17 Sep
0.40%

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

What the audience does

Likes57.7%372 in total
Reposts7.9%51 in total
Replies4.7%30 in total
Quotes2.6%17 in total
Bookmarks27.1%175 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.

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