Betfred Super League
#TheresNothingLikeLeague
233,617
Followers
690
Following
49,362
Tweets
72.5
Avg Engagement
467
Listed
No
Verified
Account Overview
What This Report Covers
This comprehensive analysis examines @SuperLeague's Twitter/X presence across multiple dimensions. We analyze tweet content, engagement patterns, audience demographics, posting habits, and network connections to provide actionable insights about this account's social media strategy and influence.
Account Classification
- Account Size: Macro Influencer (100K+)
- Account Age:2009-04-24
- Verification:No
- Location:Manchester, England
Data Summary
- Tweets Analyzed:3,250
- Avg Likes per Tweet:60.3
- Avg Retweets per Tweet:12.2
- Followers Analyzed:50,000
Engagement Analysis
Based on 3,250 tweetsThe account has a high average engagement rate with 1,267 likes and 189 retweets per tweet, indicating a loyal and active audience.
60.3
Avg Likes/Tweet
12.2
Avg Retweets/Tweet
196,106
Total Likes
39,504
Total Retweets
Engagement Quality Analysis
Median-based metrics that resist fake virality and outliers.
26
Median Likes
5
Median Retweets
82
75th Percentile
3,164
Top Tweet
0.31
Engagement per 1K Followers
Normalized influence metric
Long Tail
Engagement Pattern
Mix of hits and regular posts
Posting Behavior
Content style and format preferences
Broadcaster
Primarily shares original content
70.4%
Original
6.8%
Replies
20.4%
Retweets
2.4%
Quotes
0.0%
Threads
0.2%
With Media
72.6%
With Links
61.9%
With #Tags
58.4%
With @Mentions
85.0%
With Emojis
111
Avg Length
Audience Reaction Profile
Broadcast
Focuses on original content over replies
0.1
Reply/Original Ratio
0.12
Quote/RT Ratio
Posting Rhythm
Somewhat Bursty
Occasional posting spikes
68.7%
Weekday Posts
31.3%
Weekend Posts
Top Hashtags
Most Mentioned
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Content Strategy Analysis
Based on 3,250 tweetsContent Type Distribution
Content Breakdown
What This Reveals About Their Strategy
This is a content-creator focused account that primarily shares original thoughts, ideas, and media. With original content dominating their feed, they position themselves as a source of new information rather than a curator. This strategy works well for establishing thought leadership.
The 662 retweets (20% of activity) show they also amplify content from others, which helps build relationships with other accounts and provides value to followers by surfacing relevant content.
Posting Patterns & Optimal Timing
Based on 3,250 tweetsEngagement by Hour (UTC)
Posting Frequency Over Time
Timing Insights
Understanding when an account posts and when their audience is most responsive provides valuable competitive intelligence. The charts above show when this account's content receives the most engagement, which often correlates with when their specific audience is most active on the platform.
Peak posting times vary significantly based on an account's audience demographics, timezone distribution, and content type. Accounts with global audiences often see engagement spread across multiple time windows, while those with regional focus may have more concentrated peaks.
Audience Demographics
Based on 50,000 followersThe audience appears to be football fans and followers of sports events, particularly those interested in league and club achievements.
Average
Audience Quality
27.9%
Suspicion Index
39.3%
Low-Quality %
17
Median Reach
Audience Quality Signals
Lower percentages indicate healthier, more authentic followers.
50.4%
Empty Bio
39.2%
<10 Tweets
7.9%
Mass Following (>2K)
0.0%
New (<90 days)
52.5%
Low Ratio (<0.1)
0.0%
New (<180 days)
92.3%
No URL
28.2%
<3 Tweets
Free Mode Analysis: These quality metrics use heuristic signals (empty bios, low tweets, suspicious ratios). For ML-powered bot detection with 95%+ accuracy, use our dedicated tool.
Try Bot DetectorFollower Reach & Influence
This account's followers have their own audiences, creating potential for secondary amplification.
37,329,262
Total Potential Reach
17
Median Follower Reach
111
75th Percentile Reach
4.0%
>1K followers
0.4%
>10K followers
0.1%
>50K followers
0.1%
>100K followers
Creator vs Consumer Split
Classification based on tweet activity: Creators (>100 tweets), Consumers (10-100 tweets), Dormant (<10 tweets).
39.6%
Creators
19,798 accounts
21.2%
Consumers
10,582 accounts
39.2%
Dormant
19,620 accounts
Follower Influence Distribution
How many followers do their followers have?
Follower Account Age
How long have followers been on Twitter?
Geographic Distribution
Top locations where followers are based (from those who share location data):
125
Verified Followers
0.12% of total
7,992
Protected Accounts
7.91% of total
6 years
Avg Account Age
of their followers
Notable Followers (By Influence)
Top accounts following this user, sorted by their follower count:
| Username | Followers | Profession | Interests |
|---|---|---|---|
| @JohnCena | 14,100,315 | Wrestler | Wrestling, Motivation, Self-promotion |
| @Sporf | 1,386,923 | Sports Media | Sport, Conversation, Social Chain Group |
| @ODDSbible | 709,453 | Betting | Betting, Community, LADbibleGroup |
| @clarebalding | 685,136 | Broadcaster | sport, dogs, walking |
| @adamhillscomedy | 613,123 | Comedian | Comedy, TV, Writing |
| @DarrenBent | 541,796 | - | - |
| @BoyGeorge | 504,985 | Singer | Music, Fashion, LGBTQ+ |
| @THE_THEO_FORD | 469,285 | - | - |
| @MaggieHeroDog | 411,169 | Service Dog | Golf, Music, Service Dogs |
| @zammit_marc | 387,652 | Filmmaker | Film, Acting, Producing |
Top Keywords in Follower Bios
Top Hashtags in Follower Bios
37,905
Followers With Bio
63,086
Followers Without Bio
37.53%
% With Bio
62.47%
% Without Bio
Why Audience Demographics Matter
Understanding who follows an account reveals the type of influence they hold. Accounts followed primarily by users with few followers indicate broad, mainstream appeal. Those with many high-follower followers have greater potential for content amplification through secondary sharing.
Account age distribution also tells a story: a follower base with many new accounts might indicate recent viral growth, while established followers suggest long-term, stable influence. Geographic data helps understand the cultural context and timezone spread of the audience.
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Buy Tweets - from $15Viral Tweets
Top 5 by likes+RTs from 3,250 analyzed10,090
Combined Engagement
6,879
Total Likes
3,211
Total Retweets
2,018
Avg per Tweet
Key Metrics for Viral Tweets
0%
With Media
80%
With Hashtags
40%
With @Mentions
100%
With Links
Topical Analysis:
WORLD CLUB CHAMPIONS 🏆 Congratulations, @Saints1890 👏 #WorldClubChallenge #SuperLeague https://t.co/4fsdQIdwSw
RT @WarringtonRLFC: For Brianna ❤️ Taken far too young. Our club and town sends our condolences to her loved ones https://t.co/wbwRgzzegG
What a day ❤️ #SuperLeague #MagicWeekend https://t.co/N3CgEKwiHN
Bursting with pride 🤗 #SuperLeague https://t.co/3zTSLn4whI
Bevan French makes history 😍 All 7 of his tries as he breaks Lesley Vainikolo's #SuperLeague record for most tries in a game 👏 https://t.co/9J9GLFGVOP
Key Insights & Takeaways
The account frequently highlights major football events and achievements, which likely drives engagement and follower interaction. The use of hashtags like #SuperLeague and #WorldClubChallenge suggests a strong brand identity and community focus. The inclusion of links to specific tweets indicates a strategy to direct followers to key moments or content.
Strengths
- + Established follower base of 233,617
- + Strong original content creation
- + Active presence with consistent posting
Opportunities
- ~ Optimize posting times for peak engagement windows
- ~ Analyze top-performing content for replicable patterns
- ~ Leverage geographic concentration for targeted content
- ~ Explore collaboration potential with followed accounts
Summary
This analysis of @SuperLeague reveals an growing Twitter presence with 233,617 followers. The account demonstrates a content-creator strategy, averaging 72.5 engagements per tweet. Key strengths include consistent posting and an engaged audience, with opportunities to further optimize timing and content strategy based on the patterns identified in this report.
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