redfiona99

Game 30 itself:
Saints, despite having a pretty pants season overall, still reached the 2024 playoffs.

And somehow only lost by one point, in golden point extra time, to a Warrington team who'd had a pretty solid season overall.

A Warrington team we'd lost to twice, 10-24 and 16-2 (or a combined score of 12-40)

Full match report here: https://www.saintsrlfc.com/2024/09/28/spirited-saints-beaten-in-golden-point/

Going through the data visualisation for this game, the 30th and last game of the season.

There were 7 point scoring moments for Saints.

Who scored for Saints in Game 30?

( Under the cut )

There is something very apt about there being a Tommy Makinson special in his last game for Saints.

How many point-scoring moments the players were present for?

( Under the cut )

Which Saints players are together when Saints score?

( Under the cut )

Only 10 players are on the network graph.

( Under the cut )

The 10 players are the ones in the dark purple patch in the matrix graph.

Looking at which players were present when Saints conceded:

There were 8 point-conceding moments.

( Showing which players were present for those )

The who is present when Saints concede matrix does the same thing that the scoring matrix does, where it doesn't put all the players in the same colour together.

( Under the cut )

There are only 9 players on the concession network graph.

( Under the cut )

Rounding up the whole season

210 point-scoring moments scored
148 point-scoring moments conceded

When do Saints score:

( Under the cut )

Unsurprisingly, Percival is way in the lead, because as well as scoring tries, he's also Saints's kicker. That's also, I think, why Bennison is that high, because he also kicks when Percival can't.

Who is present when Saints score?

( Under the cut )


It is clear that there's the very often present, then a chunk of often present, followed by the injured and their replacements.

An interesting visualisation I haven't shared before, because I wasn't quite sure what it added, but I'm sharing now because as an end of season piece is when players are present at point-scoring moments.



I'm not quite sure how to interpret it, but it's interesting that the graph shapes can almost be grouped into clusters. The clusters aren't based on position, or anything obvious like that.

Looking at the which players are together when Saints score matrix at the end of the year. Following game 30, the shape has changed significantly. Where previously it had gone (radiating up from the bottom right) darkest, most often together area, then paler and paler as you move up or to the left, now there's a medium dark border at the upper left as well. This cluster has to be players who play together often when Saints score, but not as often with the darkest bottom right cluster.

( Under the cut )

I know that Knowles, Bennison and Batchelor all had either injuries, suspensions or are first reserve, which means it makes some sort of sense, but it's interesting that the pattern has only come out right at the end of the season.

The equivalent network graph looks like this:

( Under the cut )

Looking at points conceded, this is when Saints concede.

( Under the cut )

There is no obvious pattern.

Who is present when Saints concede?

( Under the cut )

Here is the shape of when the players were present when Saints conceded.



There is less of a pattern than in the equivalent figure for when players were present when Saints scored.

This is what the matrix for who is present when Saints concede.

( Under the cut )

The edges between the different areas of "played together" have got a lot fuzzier in this one over time. It still looks very much like the top left quarter of a mosaic of the sun.

Interestingly, the "dark top and left border" that the "point-scoring moments" equivalent developed is not present here.

The equivalent network graph looks like this:

( Underneath the cut )

Comparing players's position on the "present when Saints score" vs their position on the "present when Saints concede" graphs - in a purely ranking based analysis not the number they were present for, there's some interesting numbers.

The players with the greatest difference between present when Saints score vs when they concede are:
Hurrell and Percival were present for relatively fewer concession moments than scoring moments.
Whitley and Delaney were present for relatively fewer scoring moments than concession moments.

I am sure that's skewed slightly but interesting none the less.

2024 Summary:

I think, taking 2024 and 2025 into consideration, as a Saints fan I have to accept that this is one of the rough (ish) parts we take with the smooth. For whatever reason, Saints have not been playing like themselves (except in fits and starts like *that* try against Leeds in the playoffs in 2025 - https://www.bbc.co.uk/sport/rugby-league/articles/cn95qgeyn3lo).

Lack of results for Saints always worries me. Now with the IMG rankings nonsense (https://en.wikipedia.org/wiki/IMG_Grading_for_the_British_Rugby_Football_League), it worries me more, because Saints's supporter base is never going to be huge because of the size of the town. There's us, and Wigan, Leigh, Warrington and Widnes within a 40 minute driver and Oldham and Salford not much further out, so limited chance for expansion. So our "fandom" (yuck!) score will always have a ceiling, as will our ability to diversify our income streams, while the thing Saints the club do well (community work) counts for the least.

We need to do well to maintain our ranking.

As always, I am pleased to see Saints bring on young players, for instance Harry Robertson who got his debut in the 2024 away match against Wigan has gone on to be the Super League Young Player of the year in 2025.

I am very sad about some of the players who left at the end of 2024, even if it made sense for people at their various stages of life - https://www.seriousaboutrl.com/st-helens-confirm-seven-player-exits-with-one-major-name-omitted-from-list-97627/

What did I get from the season-long data visualisation project?

Not what I expected, which is a good reason to have done the project.

I was expecting a clearer separation between the players who were played often and those who weren't, because if you asked me as an external viewer I know who I would have put in each of those groups.

The data tells me I was wrong on my placement.

In terms of point-scoring, it highlighted the people I expected, and how high Bennison is really highlights the importance of conversion kicking.

I was hoping to see a pattern in when players played e.g. these two are our props for minutes 0-20, 21-40, 41-60 and 61-80, but that didn't happen, even before the injury disasters.

I did see how important the non-first team players are to covering for those injuries because you could see players moving in and out of the network graphs over the course of the season.

Experiment definitely worth doing.

If you have enjoyed reading these, the following charities are definitely worth contributing to:

The Steve Prescott Foundation - https://www.steveprescottfoundation.co.uk/

Motor Neurone Disease Foundation - https://www.mndassociation.org/get-involved/donations/rob-burrow

Yes, I am writing this post to try and distract myself from their match against Hull KR in the 2025 play-offs. (No, I have no idea how we got past Leeds.)

This game, the last of the regular season, was a narrow loss to Leigh (https://www.saintsrlfc.com/2024/09/20/saints-suffer-narrow-defeat-to-leigh/), 18-12. Now we had a try chalked off, but Leigh were the better team for the whole game and all of Saints actual points were scored in the 10 minutes that Leigh were down to 12 men because of yellow card to Leutele for attempted Welsby murder.

Because it was all in that 10 minutes, the "who played together when Saints scored" matrix chart is ... uninformative.

( Uninformative matrix chart under the cut )

So, instead, let's look at the season up to the end of game 29.

When do Saints score?

( Bar chart of when Saints score )

Who scores for Saints?



Who is present when Saints score?

( Bar chart under the cut )

Saints reached the 200 point scoring moments in this game (vs 160 point-conceding moments), so it's a good time to see if there's any players present for more point-scoring moments than point-conceding moments (or vice versa).

Percival is one of the players with the greatest differences, he is present for a lot fewer point-conceding moments, but that is because he got substituted at around minute 50.

Hurrell and Dodd are also present for relatively fewer point-conceding moments.

Delaney is the player present for relatively more point-conceding moments, as does Whitley. That I can't explain.

( Matrix graph under the cut )

There's two clear teams, the most often together (a full match day 17 - Welsby, Blake, Whitley, Bennison, Batchelor, Dodd, Percival, Bell, Hurrell, Mata'utia, Sironen, Lomax, Mbye, Clark, Makinson, Delaney and Lees) and the less often together (12 - Royle, Whitby, Vaughan, Wingfield, Walmsley, Robertson, Burns, Paasi, Knowles, Davies, Stephens and Ritson).

Oddly, only 10 players are not in the central blob, when you look at the network graph.

( Under the cut )

Looking the point-conceding moments

This was the third time Saints and Leigh played this season. Leigh 3 are about mid-way up the chart.

( Bar chart beneath the cut )

When do the point-conceding moments occur?



That these aren't in a normal curve makes me happier that the point-scoring moments truly are normally distributed rather than it being some artefact.

Who is present when Saints concede?

( Beneath the cut )

The who is present together when Saints concede matrix now looks like this:

( Underneath the cut )

The equivalent network graph looks like this

( Under the cut )

Again, the matrix and the network graph don't quite match.

Written for shauna@social.coop on Mastodon

Please don't read any further if you want to maintain the belief I know what I'm doing

I was original entranced by this post - https://gephi.org/users/quick-start/

So I downloaded Gephi and then looked for some data to interrogate. Initially, I wanted to look at how the players at Euro 2012 were interconnected - https://fulltimesportsfan.wordpress.com/2012/06/08/finalised-diagram/

It's pretty much been the same process ever since, with some learnings that have been incorporated in the oh my goodness 13 years!

Information source - I use the Wikipedia squad pages. This is why you'll occasionally see notes about "delayed because of" and a warning that the data is taken from Wikipedia so may not be accurate. The club team a player plays for is the one most likely to be wrong, especially for competitions that are between seasons. (The strange places I've ended up at on Wikipedia because of edit wars about which club someone plays for.) It is, to an extent, worse for the rugby union ones because players move at the end of the season, not the start of the next.

Because I do the input (and the removals when teams are out) by hand, I go through the national teams starting with group A and move downwards. This is why, if there is a delay in the teams in group A naming their squads, it causes a huge knock on effect. (The time Italy delayed their squad announcement till ~ 7 pm on the night of the deadline for a game show caused significant cursing because they were the first team in group A and I couldn't start till they did.)

The larger the event and the bigger the squads obviously, the longer it takes.

If you can python (I can't, one day I will learn etc), James Ashford wrote a really nice post on how to do all of this with Python - https://james.ashford.phd/2023/08/25/analysing-the-2023-fifa-womens-world-cup-with-graph-theory/

Me, I manually add things into Gephi. Sometimes this means I notice things (like the number of Zambian players playing in the Kazakh Women's League, or that there is a Saudi Women's League - https://fulltimesportsfan.wordpress.com/2023/07/22/womens-world-cup-2023-group-stage-network-diagrams/)

I use the player as the source and the national or club team as the target. I've experimented with using both directed and undirected links, and it doesn't make much of a difference.

There are other layouts, but I like the way force atlas looks. (Force atlas works okay with this size data set. For significantly larger ones, Yifan Hu is easier)

I start with the pre-set values, increasing the repulsion strength if the teams crash into each other.





I like to add each player individually, because one of the things I enjoy is seeing the shape and positions change with each addition, but I'm sure making more links at the same time would make it go quicker.

For the colour and the size of the circles, I keep it really simple and stick to number of degrees. For size, I set minimum at 10 and maximum at 50. I find it's large enough to see the small changes with each player and clear enough when I've oopsed and not attached a player to the right national team. Or the Wiki page is wrong and hasn't taken off the players that didn't make it to the final squad. Or France have decided to only pick 25 players when they could have picked 26 for who knows what reason. I also like it for the rugby union one (because they have unlimited replacements for squad injuries) because it creates a subtle gradation for each injured player.

Colour is slightly more complicated. I like to try and use either the tournament colours or the colours of the host country flag but it's not possible to change the Gephi pre-sets (or at least not as far as I can find out) so sometimes I have to go with nearest to those. I know to not complain about free software but it's the reason I keep looking to see if there's a way to do something similar in R.

I keep link width at 1.

For closest to the centre, I use the zoom function, and the degree function to get the number of players.

When teams get knocked out, I remove the players manually, hence why I try to keep the teams in order when I add them. Again, you get some fun shape and pattern changes that way.

Finally posting another "100 Great Scenes In Not So Great Films" entry means I can update the charts too.



This is one where I feel bad because the people who are linking Ad Astra to other films are not the problems with this film or the others.

3 people link Ad Astra to 4 other films.

Ad Astra is linked to Assassin's Creed by producer Arnon Milchan, who is the pink circle underneath the blue A.

Hoyte van Hoytema (the pink circle to the right of the blue B), who is an excellent cinematographer, and did I mention how good his work was in Ad Astra, links Ad Astra to Spectre and Tinker Tailor Soldier Spy.

Robert Alonzo who works in stunts and is the pink circle to the right of the blue letter C, links Ad Astra to League of Extraordinary Gentlemen. That was the link that really changed the shape of the overall diagram pulling League of Extraordinary Gentlemen in from it's point on the far right of the diagram into the centre.

The figures are obviously very simple now that there are only two teams.



The same graph, but labelled:



Gotham FC are the club team closest to the middle.

Barcelona, unsurprisingly, have the most players left, with 10. The teams with the next most players remaining are Chelsea, Arsenal and Manchester City with 6.

Manchester City, Arsenal and Gotham FC are guaranteed to have a player on the winning side.

The community view gives less information when we're down to two teams so I haven't shared that.

It's a final to look forward to.

The group stage diagrams and predictions based on them can be found here - https://fulltimesportsfan.wordpress.com/2025/06/29/euro-2025-group-stage-network-diagrams/

How did the group stage predictions go?

From group A, I predicted Norway and Switzerland would qualify for the quarterfinals, and I was right, even if it involved extra time goals in the decider.

Group B, I predicted Spain and AN other, which I am aware is a bit of a "the sky is blue" sort of prediction.

Group C, I predicted Germany and Sweden

Group D, I predicted Netherlands and one of England and France, which was oh so wrong.

So out of 8, I am willing to call that about equivalent to 5/8.

With that in mind, here are the quarterfinal network diagrams

( Four figures under the cut )

Sweden are the national team closest to the centre (just, vs. Norway). Either Lyon or Bayern Munich are the club team closest to the centre.

Barcelona are the team with the most representatives left in with 14, then comes Bayern Munich and Juventus with 13 then Chelsea with 12.

Predictions for the quarterfinals:

These are quite difficult because Italy and Spain are pulled out by how many of their players play for Juventus and Barcelona respectively, while Bayern Munich, and the players that play for them, are pulling together Sweden and Germany, and Arsenal and Chelsea are holding together England, Sweden and Norway.

Sweden vs England - Diagram says Sweden

Norway vs Italy - Diagram says Norway, plus every single women's football pundit keeps bewailing how often Italy somehow manage to screw up. On the other hand, Norway trip over their own feet also.

France vs Germany - Diagram says Germany just. Football fan says "ooooh".

Spain vs Switzerland - Remember how I said Barcelona pulled Spain out of the diagram. This really reflects that. Switzerland are far closer to the centre. On the other hand, there is no way I can see Switzerland beating this Spanish team.

Now in a slightly different format.

The Making Of:

UEFA saying teams had to announce their teams by the 25th of June meant that I have had plenty of time to make these diagrams. That the women's teams are limited to only 23 players also sped this along.

Interestingly, while coverage and interest in women's football had increased hugely, the Wikipedia pages are still updated much more slowly than the equivalent men's pages. I was making the diagrams and spotted that Italy had a much bigger and darker red circle than the other teams, and when I checked, it was because the Wikipedia page hadn't been updated following the cut from 27 players to 23.

The Diagrams:

( Four diagrams below the cut )

Some Observations Based on the Diagrams:

Every country has at least one player playing for their home league, except Wales. Every country has at least one player playing abroad - for Italy it is literally only the one (Arianna Caruso for Bayern Munich).

It is not clear if Germany or Switzerland is the country closest to the centre. It is clear that Bayern Munich are the club team closest to the centre.

The club teams with the most players present are Barcelona with 17, Bayern Munich with 16, then Juventus and Chelsea with 14.

For most teams, the players are spread over several teams. The exceptions to this are Italy, Spain, Portugal and Germany, although with Germany it's less obvious when looking at the diagram because a number of non-German players also play for them. England are more weakly like that, with a lot of players from Chelsea, Arsenal and Manchester City.

The number of links between Italy and Denmark is due to the large number of Danish players playing in Italy, which might be related to similar patterns seen in the men's game. Danish acquaintance of mine used to complain players on had to be signed by Italian clubs to walk into the national team.

Having done this for women's tournaments before (see the 2022 version of this here - https://fulltimesportsfan.wordpress.com/2022/07/08/womens-euro-2022-network-diagrams-group-stage/), I'm slightly saddened that we seem to be losing those teams where the main team is the women's team; there's no players for Turbine Potsdam for instance, and only a few from London Lionesses, Paris FC and Madrid CFF. I don't want the increased interest in women's football to cause it to lose its history. (I still don't forgive the English Women's Super League for screwing over Doncaster Belles.)

Some of the old divisions still remain; Liverpool not giving a flying curse about it's women's team is reflected by there only being two Liverpool players present, both for Wales, compared to eight for Everton. Everton have always supported their women's team - when I was young, the only chance women in my area had of playing football properly was in their women's team.

One thing that might be affecting the clustering is the number of players who play for US teams. A country with a lot of club teams represented but not present itself doesn't usually happen, unless one of the big guns doesn't qualify (looking at you, so often, Italy).

I will be keeping an eye on Poland, particularly Emilia Szymczak who is 19 and plays for Barcelona B. If you're good enough to be picked up by Barcelona at that age ...

In terms of the community view, the national teams that group together are Sweden and England (because of Arsenal and Chelsea) and Switzerland and Germany (because of Eintracht Frankfurt, Bayern Munich and Wolfsburg). Switzerland and Germany being joined is really interesting given Germany's circle overlaps with Norway's, and yet those two aren't linked.

Predictions:

L likes me to try to predict the outcome of the games from this, and there is some correlation between closeness to centre and connectedness and doing better in tournaments. However, previous experience (https://fulltimesportsfan.wordpress.com/2023/08/03/womens-world-cup-2023-last-16-network-diagrams/) has shown that it doesn't work as well for women's football.

Because half the teams will be gone after the group stage, it's a lot harder to predict, and it makes the games so much more tense. Limiting it to 16 teams also means some of those groups are stacked - like group D - England, France, Netherlands and Wales. So that's the present World Cup holders, the winners of Euro 2022, the winners of Euro 2017 and the lowest ranked team in the competition.

Bon chance, Wales.

(Actually the diagram doesn't have them as separated from the other teams as I would have expected.)

Spain are the team that really confuses the diagram. I would expect them to be a lot more central, given I expect them to do well. It could be the number of players that also play for clubs with Portuguese players that is pulling them out there or potentially a sign that they may not do as well as expected.

Running purely off the diagram, the teams most closely clustered are Germany, Norway, Switzerland, Sweden, Netherlands, England, France, and then one of Denmark and Spain.

You'll notice that's 9 teams and only 8 go through.

If we take Switzerland and Norway from group A, that leaves 3 more groups where it is unclear.

For group B, it is unclear because only Spain are in that central core, and they're barely in it.

For group C, as Germany and Sweden are closer to the centre than Denmark, I will predict that these are the teams that will get through.

For group D, D for death, that logic can't work because England and France are a similar distance from the centre of the cluster. My best prediction - that group is going to be tight.

Game 26 was Saints away at Huddersfield, which Saints won (https://www.saintsrlfc.com/matches/2024/first-team/huddersfield-giants-v-saints-2024-09-01/?swcfpc=1)

The biggest news to my mind was Morgan Knowles coming back. I was not alone in this opinion - https://www.sthelensstar.co.uk/sport/24556400.morgan-knowles-brought-saints-return/

The St. Helens Star, a biased source I grant, said, "He missed the best part of three months with a groin issue – a period that coincided with the beginning of Saints’ picking up other injuries and then subsequent run of defeats." (https://www.sthelensstar.co.uk/sport/24556400.morgan-knowles-brought-saints-return/)

It also points out he then missed 3 games due to a ban for a high tackle.

Saints lost 7 of the 11 games Knowles missed.

The really terrifying thing is that 2024 was Knowles's 10th year with Saints. Time flies, eh?

While the game was a victory for Saints, it also highlighted a worrying trend for yellow cards (although I forgive Noah Stephens entirely).

Bennison having to do the kicks reassured me in the "there is another" with regards to kicks if Percival is off the pitch.

None of match-specific pictures are all that interesting so I'll move on to the season to date diagrams.

Seeing Bennison shoot up the "who scores for Saints?" diagram after just one game shows how important the kicker is.

( Under the cut )

Robertson is now on the list after scoring his first ever try for Saints. Overall 24 different players have scored either a try or conversion for Saints in 2024.

When do Saints score?
( Under the cut )

Who is present when Saints score?

( Under the cut )

The matrix of who is present when Saints score is interesting:



Normally it would go darkest (most often together) in the bottom right hand corner and paler (less often together) as it moves up and to the left.

This time, that pattern happens but then there's a suddenly dark border along the top and left which consists of Whitley, Bennison and Batchelor, suggesting Saints score when they are on the pitch together. Which makes some sort of sense because Batchelor definitely missed some matches with an injury.

The equivalent network graph is slightly different again.



It's interesting that two ways of presenting the same data give subtly different results.

There's no real changes to the pattern of the "who scores against Saints" diagram so I haven't included it.

The last 10 minutes of the game is still when Saints are most vulnerable.

( Bar chart under the cut )

Blake also leads the "who is present when Saints concede?" chart

( Bar chart under the cut )

The matrix diagram of who is present together when Saints concede is not as pretty as it was after game 25 (https://fulltimesportsfan.wordpress.com/2025/06/11/saints-ahoy-game-25-and-the-2024-season-to-date/). It think it's because the "curls" of more often together in the mid-section are less well defined than they were last time.

( Under the cut )

Unlike the "who is present when Saints score?" matrix and network graphs, the concession network graph mostly matches the matrix diagram.

( Under the cut )

Game 24 of Saint's 2024 season was their Magic Weekend game.

I quite like Wikipedia's description of Magic Weekend - "an annual event organised by the Rugby Football League in which an entire round of Super League matches is played over a weekend at a single stadium to promote the sport of rugby league." (https://en.wikipedia.org/wiki/Magic_Weekend)

I've been lucky enough to go twice, both in Newcastle.

The only downside to Magic Weekend is, that in order to sell tickets, Super League tend to have teams play their local rivals. Which means you can end up playing the same team far too often.

And playing Wigan, again, in a year your team are already not doing well, is far, far too often.

When Saints then lose, 20-0, to Wigan, that's the pits - https://www.saintsrlfc.com/2024/08/17/saints-beaten-on-derby-day-at-magic-wknd/

On the other hand the referees let this sort of thing go:
( Picture under the cut )

Understandably, there can be no diagrams for Saints's point-scoring moments in this game, nor are their any updates to their point scoring moments for the season.

There is no pattern to when Saints conceded, except maybe a slight suggestion that they concede more in the last 10 minutes (but so does everyone else).

( Bar chart under the cut )

The "who is present when Saints concede" diagram is so weird that it made me double check that I'd not made some sort of data entry error.



It makes sense, just, if you consider that he's about the only player who didn't have an extended injury / suspension break.

It does make the "Who is present when Saints concede" matrix look intriguingly different.

( And makes the alt text tricky to write )

The equivalent network graph is shaped like a fox's face.

( Under the cut )

As Royle, Walmsley, Wingfield and Burns are the only players sticking out, it tells me that the other players who had previously been in the little "rarely but when they do they play together" sticking out blobs have now been subsumed into the central blob. This is true, as they are now at the edges of the central blob (Paasi, Vaughan and Stephens on the left hand side and Bennison, Knowles and Batchelor on the right).

That change is most interesting, and suggests Saints have had to start leaning on the full squad of players.

(Yes, I know we're almost at the end of the 2024-2025 season, work with me here)

One major thing, for the purposes of this update post, did happen since the 2022-2023 version of this post. Harry Kane moved to Bayern Munich.

This was probably a very good thing for him (something is definitely rotten in the state of Tottenham Hotspur).

And I've previously included stats from non-Premier League football for Salah, so it's definitely doable method-wise.

It does mean that it will no longer be as direct a comparison, but I think that's okay.

On to the graphs:

First, we have percentage of games played, up to the point of being 30 for all three players. This comes first because the original question L posed was whether Kane's putatively dodgy ankle would allow him to catch Shearer's Premier League goal tally.

Looking at up to 30, Shearer's curve has a serious down curve here, because 30 was when he had his second big injury (carrying that Newcastle team).

Although Salah and Kane's curves have different shapes, they are now converging on the same point in terms of percentage of games they played at 30. Whether this is changes in how coaches use players, resting them more often now to allow more games overall, I don't know.

( Under the cut )

Using the "extrapolated till age 35" data, the curves look like this:

( Under the cut )

It's Salah's curve that shows the greatest drop here (because at 31 he had an injury that kept him out of several games [Hamstring tears are no one's friend), while Shearer and Kane's curves converge to similar points.

Now Goals per game

Using the data up to everyone reaching 30

( Under the cut )

Again, Shearer is the odd one out because of his injury, although to me, the interesting thing is how similar the shape of the curves are for Kane and Salah. The curve also makes it clear that, although Salah is indeed a very awesome striker, Kane is the one who is more of an out and out goal getter. Plus, the entirely ridiculous stat that Kane had a goal per game in 2023-2024.

Using the extrapolated data, Kane stands out:

( Under the cut )

Kane's curve ends much higher than the other two, possible because of that ridiculous 2023-2024 season, with Salah's curve going down because of his injury in 2023-2024.

Finally, we have the goals per possible game, which would normally perk Shearer's stats up because he had fewer opportunities to have games to score goals in, but, as I said, his injury in the year he was 30 meant his curve really does parabola downwards in this graph.

( Below the cut )

The other two curves, again have the same shape, but Kane is higher again.

On the extrapolated till 35 goals per possible game

( Below the cut )

Shearer and Salah have similar trajectories, possibly because they both had bad years around 30/31 with injuries, while Kane's curve has gone shooting off at the top because of his excellent 2023-2024.