A brand new Fantasy Premier League Podcast using statistics that I have created to optimize the best team and earn more points.
Today I discuss the top 10 FPL point scorers so far this season and predict who will be the top 10 FPL point scorers (based on the underlying data) come the end of the season. Then I review GW18 (meh…
Today I discuss the top 10 Bukayo Saka replacements going into GW18, based on the stats with a bit of my own personal bias and opinion thrown in. I review my GW17 performance (why are all my points o…
Today I discuss about 10 or so moves people are making in the FPL meta that I think are mistakes. Then I discuss my GW16 performance (can't seem to string 2 green arrows together), and plan for GW17.…
Today I discuss the newly revealed Mystery Chip: The Assistant Manager Chip. I walkthrough my gut reaction, some strategies and potential pitfalls. Then I talk through my GW15 performance and boring …
Today I review my GW14's performance along with a general discussion of all the weird stuff to happen midweek, and discuss my plans for GW15. I bring up a useful tool (livefpl.net) that I use every n…
Today I discuss my team by reviewing GW13's performance and discuss my plans for GW14 which has a deadline of TODAY. I even discuss my tentative plan for GW15 which is Saturday. After I discuss my te…
Happy Thanksgiving everyone! We are back to discuss why context in statistics is important, and why content creators should be more transparent about the statistics they produce. I walk through how t…
Another international break, another Wildcard Episode. Today I discuss Game Week 12 Wildcard, walking through several different team structures algorithmically optimized using the FPLOptimiseR packag…
Today go through an indepth planning of GW 12, GW 13 and beyond. Walking through a few different options. Also I introduce a new Discord Server for the podcast and general FPL chat. Come join the cov…
Today I rank the top 15 attacking FPL assets (Forwards + Midfielders) based on a combination of value added per million (VAPM), VAPM using the underlying data model, explosiveness, captaincy opportun…
This is a part 2 follow-up on the casestudy of stupid mistakes in FPL from last episode (Gameweek 8 to Gameweek 9). I evaluate every iteration of my stupidity and walk through the thought process of …
Mistakes have been made. It is time to do a deep dive into my mistakes: Why am I making early transfers, and why am I compounding that by taking a (-4) hit? Today's episode is more of a case study in…
Over yet another international break I discuss Game Week 8 Wildcard, and how it differs from Game Week 4 WC all within the context of Value Added per Million (VAPM). I turn back to the FPLOptimiseR p…
Buckle in, it is a long one today. I discuss performances from every single team in the Premier League so far this season and give my top 3 FPL assests from each team. All stats provided in this epis…
Today I discuss the concepts of the eye-test, and compare players who pass or fail the 'eye-test' in the context of their underlying and observed statistics. This one is a very subjective topic so fe…
Today I talk through suggested players for Wild Card Game Week 6 using Value Added per Million (VAPM), use the FPLOptimiseR package in R to examine algorithmically optimized teams, review my team fro…
Today make a weak defense of the game week 4 wild card, and why it may not have been such a bad idea. I discuss variance and try to make sense of the incredibly weird week in FPL just past. Later I r…
Today I take wild cards for game week 4 a step further and use algorithmic optimization using the FPLOptimiseR package in R. Looking ahead to this weekend I discuss a captaincy deep dive to compare S…
Today I talk through general Wild Card Strategies ahead of Game Week 4 using Value Added per Million (VAPM), review the massive (and unexpected) green arrow from GW 3, and discuss my WC draft for GW …
Fantasy Premier League strikes again for Game Week 2. Today I look ahead to Game Week 3 and beyond to discuss high VAPM (Value Added per Million) players, what the data is telling us about them, and …