Data-Driven Fantasy Football: Identifying Imminent Attacking Returns - Episode Hero Image

Data-Driven Fantasy Football: Identifying Imminent Attacking Returns

Fantasy Football Scout - FPL Tips · · Listen to Original Episode →
Original Title:

TL;DR

  • Erling Haaland's underlying data suggests he is due for more goals, as he has missed four big chances in his last four matches despite 12 shots, indicating a potential captaincy option.
  • Declan Rice consistently provides 4+ points per game due to his involvement in set pieces and chance creation, making him a reliable, cost-effective midfielder despite limited goal contributions.
  • Mateo Kovačić's underlying stats show potential for attacking returns, with eight shots and two big chances missed, suggesting he could explode offensively if his accuracy improves.
  • Bryan Mbeumo's high volume of shots and shots on target indicate attacking threat, but his upcoming departure for AFCON makes him a short-term option rather than a long-term transfer.
  • Daniel Malen's impressive attacking output despite limited minutes, with nine penalty area attempts and a high xG in recent appearances, makes him a differential pick for braver managers.
  • Marcus Senesi's consistent defensive contributions and recent assist highlight his value as a 5 million defender, offering attacking potential beyond clean sheets.
  • Elliot Anderson offers steady, reliable returns of 4-6 points, making him a valuable "plug and play" midfielder who can contribute even in difficult fixtures.

Deep Dive

This episode of Fantasy Football Scout's "Goals Imminent" podcast focuses on identifying players whose underlying performance data suggests they are due for attacking returns, even if their recent output has been inconsistent. The core argument is that by analyzing metrics like expected goals (xG) and chances created, managers can identify undervalued assets and make smarter transfer decisions heading into Game Week 16, particularly with the upcoming African Cup of Nations (AFCON) in mind.

The analysis highlights several key players. Erling Haaland, despite being a consistent performer historically, is noted for missing multiple big chances and having a lower goal return in recent matches, suggesting a potential opportunity for managers to captain him against Crystal Palace if they believe he is due for a significant haul. Conversely, players like Mateo Kovačić are identified as having strong underlying stats that are not yet reflected in their points totals, making them intriguing prospects, especially as other midfielders depart for AFCON. Daniel Munen's impressive efficiency despite limited minutes is also noted, presenting a differential option for braver managers. The discussion also touches on the consistent, albeit less spectacular, returns from players like Declan Rice, who offer a reliable floor of points and potential for bonus points through assists and clean sheets, making him a solid and affordable midfield option.

The second-order implication of this data-driven approach is a shift in fantasy football strategy towards proactive asset selection rather than reactive adjustments. By identifying players whose data suggests an impending return, managers can gain an edge over those who only react to recent points hauls. This also means that players who have historically performed well but are currently underperforming on the "Goals Imminent" table, like Haaland, may still be strong captaincy options. The impending AFCON also creates a dynamic where players leaving for the tournament will free up minutes and potentially create opportunities for those remaining, such as Kovačić, to increase their attacking output. The podcast implicitly suggests that understanding these data trends can help managers navigate fixture congestion, potential rotation due to European competitions, and the impact of player absences, ultimately leading to more consistent green arrows and a higher overall rank.

Action Items

  • Audit player performance: For 5-10 players with high expected goals but low actual goals, analyze underlying metrics to identify potential for future returns.
  • Track player consistency: For 3-5 midfielders consistently scoring 4+ points, calculate their correlation with team set pieces and bonus point potential.
  • Evaluate player minutes: For 2-3 players with significant expected minutes but inconsistent starts, assess their impact on team performance and potential for increased playtime.
  • Analyze player transfer impact: For 3-5 players recently transferred out, track their subsequent performance to refine future transfer decisions.
  • Measure differential player success: For 2-3 players with low ownership but strong underlying metrics, track their performance against established options to identify potential value.

Related Episodes

Key Quotes

"agents are everywhere automating tasks and making decisions at machine speed but agents make mistakes just one rogue agent can do big damage before you even notice rubric agent cloud is the only platform that helps you monitor agents set guardrails and rewind mistakes so you can unleash agents not risk accelerate your ai transformation at rubric com that's r u b r i k com"

This quote introduces Rubrik Agent Cloud as a solution for managing and mitigating risks associated with AI agents. The speaker highlights the potential for agents to make errors and cause significant damage, positioning Rubrik's platform as a way to monitor, control, and correct these mistakes, thereby enabling the safe adoption of AI.

"it went well um yeah 10th green arrow in a row i cannot believe it into the top 200k which is the first time but hopefully that will continue um so i've been very steadily gone over that period from like one and a half million to wherever i am now you know the 10th of that"

This quote reflects the speaker's personal success in Fantasy Premier League (FPL), detailing a streak of ten consecutive "green arrows," which signify a positive rank movement. The speaker expresses surprise and satisfaction at their progress, moving from a rank of 1.5 million to within the top 200,000, indicating a consistent and effective strategy.

"well yeah we're both on 10 consecutive green arrows ooh what's happening yeah it's had an probably my best ever season probably because the have been previous finishes at like 16k and 28k which you know even they aren't phenomenal over like 10 15 years but the good but to be around 30k now wow that's great that's absolutely huge"

This quote shows Joe's FPL success, mirroring Mark's achievement with ten consecutive green arrows. Joe expresses that this season is likely his best ever, surpassing previous top finishes of 16k and 28k, and is currently around the 30k rank, which he considers a significant accomplishment.

"but haaland on here four big chances missed every one only scored one goal in the last four matches five shots on target 12 shots all bar one inside the box yep he should have got uh two more goals he should be on three goals over this period and not on this table at all"

This quote analyzes Erling Haaland's recent FPL performance, noting a discrepancy between his underlying statistics and actual goal output. The speaker points out missed big chances and a low goal return in the last four matches, suggesting that based on his shots and expected goals (xG), Haaland should have a higher tally and not be appearing on a table for underperforming players.

"i remember that one goal game where he got like 20 or 30 shots or well something ridiculous like that um which may have skewed his stats a little bit but yeah he's still a great option but he's not a really great option and when we got five afcon transfers you want a really good option i think"

This quote discusses Jean-Philippe Mateta's FPL performance, acknowledging his potential but questioning his current value as a top option. The speaker recalls a past game where Mateta had a high volume of shots for a single goal, suggesting this might have skewed his overall statistics, and concludes that while still a good option, he may not be a "really great" one given the context of limited transfers and the need for high-performing players.

"i think the problem with fernandes when he missed penalties and that wasn't getting returns people were looking his price tag at well over 8 million and thinking ah now they're not because he's obviously getting those returns but i think with rice at 7 million you can you can sort of put up with a two pointer every now and again because i think most of the time he's going to get a bit more than that and that's why he's the second just behind bruno fernandes so he's a really he's a really strong really strong player to get in definitely to add a bit of depth to that"

This quote compares Declan Rice's FPL value to that of Bruno Fernandes, highlighting Rice's consistent performance at a lower price point. The speaker notes that while Fernandes faced criticism for missing penalties and not returning points at a higher cost, Rice at £7 million offers reliable returns, making him a strong and valuable addition to FPL teams, even if he occasionally scores low.

Resources

External Resources

Books

Videos & Documentaries

Research & Studies

  • 'Goals Imminent' and 'Assist Imminent' tables - Used to identify players due for attacking returns based on underlying data.

Tools & Software

Articles & Papers

People

  • Marc - Co-host of the "Goals Imminent" podcast.
  • Joe - Co-host of the "Goals Imminent" podcast.
  • Ollie Watkins - Player discussed in relation to minute sharing and potential injury.
  • Eze - Player mentioned as a successful differential pick.
  • Tom Freeman - Colleague mentioned for picking differential midfielders.

Organizations & Institutions

  • Fantasy Football Scout (FFS) - Host of the "Goals Imminent" podcast.
  • Manchester United - Opponent team mentioned in relation to player performance.
  • Everton - Opponent team mentioned in relation to player performance.
  • Tottenham - Opponent team mentioned in relation to player performance.
  • Wolves - Opponent team mentioned in relation to player performance.
  • Bournemouth - Opponent team mentioned in relation to player performance.
  • Aston Villa - Team discussed for squad depth and fixture analysis.
  • Crystal Palace - Opponent team mentioned in relation to player performance.
  • Manchester City - Opponent team mentioned in relation to player performance.
  • Fulham - Team discussed for player price range and performance.
  • Nottingham Forest - Team discussed for player price range and performance.
  • West Ham - Opponent team mentioned in relation to player performance.
  • Arsenal - Opponent team mentioned in relation to player performance.
  • Chelsea - Opponent team mentioned in relation to player performance.
  • Liverpool - Team mentioned in relation to penalty takers.
  • Newcastle - Team discussed in relation to player performance.
  • Sunderland - Opponent team mentioned in relation to player performance.
  • Brentford - Team mentioned for having good upcoming fixtures.

Courses & Educational Resources

Websites & Online Resources

  • FFSCOUT PREMIUM (https://bit.ly/FFScoutPOD) - Link for premium content.
  • Twitter (https://x.com/ffscout) - Social media platform for FFS.
  • Bluesky (https://bsky.app/profile/ffscoutfpl.bsky.social) - Social media platform for FFS.
  • TikTok (https://www.tiktok.com/@ffscout) - Social media platform for FFS.
  • Instagram (https://www.instagram.com/ffscout_/) - Social media platform for FFS.
  • Facebook (https://www.facebook.com/fantasyfootballscout/) - Social media platform for FFS.
  • WhatsApp (https://www.whatsapp.com/channel/0029VbA2A9PDTkK5r73WyM1g) - Communication channel for FFS.
  • podcastchoices.com/adchoices - Website for ad choices.
  • amazon.com/amazonprime - Website for Prime membership details.

Podcasts & Audio

  • Fantasy Football Scout - FPL Tips - The podcast series featuring the "Goals Imminent" episode.
  • NBA on Prime - Mentioned as a programming highlight on Prime Video.

Other Resources

  • Agents - Discussed as automated entities making decisions at machine speed.
  • Rubric Agent Cloud - Platform for monitoring agents, setting guardrails, and rewinding mistakes.
  • AI transformation - Concept discussed in relation to business acceleration.
  • FPL (Fantasy Premier League) - The game discussed throughout the podcast.
  • Green arrow - Term used to indicate a positive rank movement in FPL.
  • xG (Expected Goals) - Metric used to assess goal-scoring probability.
  • AFCON (Africa Cup of Nations) - Tournament mentioned in relation to player availability.
  • Deffcon - Metric used to assess player performance and potential.
  • Bench boost - An FPL chip that doubles points from bench players.
  • Free hit - An FPL chip that allows unlimited transfers for one gameweek.
  • Penalty takers - Role discussed in relation to specific players.
  • Champions League - Competition mentioned in relation to player availability and penalty takers.
  • European lineup - Refers to the team selection for European competitions.
  • FA Cup - Competition mentioned in relation to team selection strategies.
  • Bonus points - Points awarded in FPL based on performance metrics.
  • Underlying data - Statistical information used to predict player performance.
  • Underperformed - Term used when actual performance is lower than expected based on data.
  • Overperformed - Term used when actual performance is higher than expected based on data.
  • Differential - An FPL player with low ownership but high potential.
  • Rotation - Refers to the changing of players in a team's lineup.
  • Clean sheet - Achieved by a team when they concede no goals in a match.
  • Set piece - A dead ball situation in football (e.g., corner, free kick).
  • Corners - A type of set piece.
  • Free kicks - A type of set piece.
  • Penalty kicks - A type of set piece.
  • Underlying stats - Data used to evaluate player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • Underlying data - Statistical information used to predict player performance.
  • **Underlying

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