Comparison of statistical packages Puns

Ter Stegen in comparison to other top goalkeepers in distribution with numbers per 90

Following the previous post where I compared goalkeepers on their goalkeeping abilities, I decided to make another post where I focused more on their distribution and how Ter Stegen compares in that department with other top goal keepers.

These numbers show that Ter Stegen is among the best when it comes to distribution and there's very few that come close to him, although his numbers this season have decreased compared to the seasons before. One interesting thing is that the numbers of long passes attempted have decreased from 2018 to 2021 (although their accuracy have increased) but so far those numbers might be at an increase. Another interesting aspect is how short passes have increased by a lot these past 3 years compared to the 18/19 season.

One thing to keep in mind is that the playing style of a team is a big factor in these numbers, specially in the number of passes attempted while the percentage of completed passes can be more relied on a goalkeeper and their judgement on where to pass a ball and do it accurately. These numbers are also just part of what distribution consist of and it's also near impossible to measure distribution into numbers due to so many varying factors, albeit the numbers can give a hint.

Glossary:

• TotCmp - Total passes completed per 90.
• TotAtt - Total passes attempted per 90.
• Cmp% - Percentage of passes completed per 90.
• TotDst - Total passes distance per 90.
• TotPrgDst - Total progressive distance per 90.
• ShortAtt - Short passes attempted per 90.
• ShCmp% - Short passes completed per 90.
• MedAtt - Medium passes attempted per 90.
• MedCmp% - Medium passes completed per 90.
• LongAtt - Long passes attempted per 90.
• LongCmp% - Long passes completed per 90.

The top two best numbers for each relevant column are coloured green, the two middle are yellow and the two worst numbers are red, just for easier readability.

Stats were gathered from FBref.com.

Title might not be that accurate and i apologize for that.

Here I've taken the list of Players who have scored over 5000 Test Runs and have a Career Batting Average of 50+ and compared their Records to their Peers in Tests where their Team had a Batting Average of less than 25 in the Match.

So to better put it i've considered matches where the Player's Team scored less than 500 Runs for the match as that would have the Team's batting Average less than or equal to 25. I've Looked into Player's contributions in each match where the team's average was less than 25 and compared the overall Player's Average to the Overall's Team Average.

I just decided to do this since there were a lot of low scoring tests happening and i just wondered which batsmen in general did better in these games compared to the others. I've kept the criteria very high for the players as collecting the Data involved a lot of Manual Work and i was getting tired at the End. So here goes:

First the Records of the Batsmen in Test Cricket:

Hello. Had this simple idea in my mind and wanted to share the results. Cheers.

It's insane that nearly half of trebles happened in a 7 season span. There were 4 treble winning sides from 2008-09 to 2014-15.
Also 1998-99 season was destined to be on the list with 2 sides with domestic doubles playing in the UCL final.

Just want to preface this by saying that K/Ds only don't paint an adequate picture of a players/teams performances. I just really like Excel and COD Competitive so I decided to play around with the new Player Profile feature on Breakingpoint.GG (s/o to those guys for providing stuff like this for me to play around with)

The following graph is a statistically analysis I put together that measured the average difference in K/D from Group Play to Major Play. A quick breakdown of the graph. The Y-axis represents a players overall K/D throughout the entirety of the season. The X-axis represents a total average change in a player's K/D from group to major play across the entirety of the season. The only criteria I included for players was that they must have played at least three full group and major stages.

Some quick analysis on the data:

1. You'll see that players like Simp, Abezy, Cellium, and Insight are on the left side of the graph. Due to their high K/Ds during group stages they are bound to see a drop off in the slaying production. But with none of those four players crossing the -0.10 threshold, I think this graph highlights their consistency as premier slayers.

2. Aqua is the biggest loser in this analysis, seeing an average -0.15 change in his K/D from group to major play. Makes you think what Paris could've done if he was able to maintain his slaying ability at the Major.

3. On the flip side, PaulEhX completely picks it up in the slaying department at the Majors. Even though it's nice to see an increase in production, having that same type of slaying ability in the group stages could've served the Royal Ravens well.

4. I was surprised to see such a drop off in slaying from Envoy from Group to Major play. I'm interested to see if he can maintain his level of slaying he displays in the Group Stage at this Major.

5. Illey is the player I am going to pay attention to the most during the Major. He has struggled in the slaying department all year and typically trends downward at Major events. Dallas are looking in peak form and if Illey can turn his slaying woes around at this Major I would not be surprised if Dallas runs away with this Major.

https://preview.redd.it/jxyhy04ct2e71.png?width=1906&format=png&auto=webp&s=277be83177c90fcb6644725799db5fc40a655fd1

Now that we know Scottie is way ahead of his expectations offensively, and are well aware of his advanced defensive game; what are your expectations for him, say, 3-5 years from now?

Is he capable of averaging 25+ PPG? Could he average 10+ rebounds as well? Which player past or present would you liken him to?

I’ll do a statistical comparison of the top upvoted comments here, including things like peak season, peak stretch, and career averages. No troll answers.

In light of everything, I've prepared an exhaustive statistical comparison of Steph vs Durant. I didn't cherry pick stats. It takes a little while to get there, but Steph is better.

Regular Season Peak

Playoffs

We're looking at the two most efficient scorers in NBA history, so Steph doesn't have his normal huge rTS (TS relative to the league) advantage. He still has a passing advantage, however, which puts him slightly ahead in OBPM. Durant makes this up and then some on the defensive end, which puts him slightly ahead in box stat analytics.

Playoff numbers are about equal, though it should be noted that Durant's best playoffs come next to Curry. Which i A) don't think is a coincidence, and B) don't think works both ways in equal measure. More on that later.

###BPM 2013-2021 (Box Score)

Looking at the numbers over the course of both of their peaks, you can see that Durant was the clear better player pre-2015, Steph caught up, and has arguably been better over the last 3 seasons, as Durant has seen some all-around decline (and this year, injury problems). Still, over the last 8 years, Durant is slightly ahead in personal box impact.

Team Impact Stats

While Durant has a slight advantage in box score analytics, largely due to his size/defense, Steph's constant movement and gravity makes his teammates better in a way that Durant can't compete with. Looking at the teammate and luck-adjusted +/- analytics of each:

###EPM (adjusted +/- impact stats)

Durant peaks as an all-around force in 2014, with a +7.1 EPM. Steph has beat that number in 5 of the next 6 seasons. Since 2015, Curry has never finished below Durant in team impact, including when they played together.

###OEPM (+/- imp

Here i've tried to compare a very good list of ODI Batsmen to their Teammates. For the comparison i've selected a list of Batsmen who have scored over 4000 Runs and and average 40+ against the Top 8 ODI Teams.

For Comparison i've considered the matches where the Said player was playing and compared his Average and Strike Rate to the Rest of the Team's Average and Strike Rate.

List of 30 Batsmen make the Criteria Cut of 4000 Runs and 40+ Average vs the Top 8 Teams.

Following is the list sorted by how better they Averaged compared to their Teammates.

Full text here: NorthWest Biotherapeutics Inc. (NWBO): I have updated my Table that compares the (advfn.com)

https://preview.redd.it/xlledab7bxu61.jpg?width=924&format=pjpg&auto=webp&s=34e18f9693a39c23221f47d5e59e95028b7aca14

Preface: On April 14, 2018. When asked about the upcoming first round match-up with rookie Donovan Mitchell, Paul George replied: "Y'all ain't met Playoff P yet, huh?"

In a sign of things to come, Mitchell ended up outscoring PG (28.5 to 24.6) and eliminated the Thunder 4-2.

With yet another extremely underwhelming playoff performance, it made me look into his stats before and after him coining himself the now-infamous nickname.

> Paul George's per game playoff stats before calling himself Playoff P:

• 18.9 points/7.26 rebounds/3.9 assists on 42.3/36.5/80.3 shooting splits.

But this statline also takes into account PG's first few years which isn't correctly indicative of the Pacer PG who was on the rise to superstardom. So, here are his stats in his 'prime' in Indiana.

> Prime Pacers Paul George's (2013-17) per game playoff stats before calling himself Playoff P:

• 24.4 points/7.7 rebounds/4.4 assists on 43.3/41.1/84.6 shooting splits.

And now finally, > Paul George's per game playoff stats after calling himself Playoff P:

• 23.08 points/6.58 rebounds/3.45 assists on 40.9/33.8/86.4 shooting splits.

Conclusion: After calling himself "Playoff P", PG was worse across the board in every offensive stat except FT shooting. And even without the adjustment, PG was worse in every stat except points per game and FT shooting.

Also interesting to note:

• Playoff P was handed a first-round defeat by the Utah Jazz averaging 24.7 points per game.
• Jamal Murray against pretty much the same core with the same coach but with much more experience averaged 31.57 fricking points per game and proceeded to the next round.

Stats taken from BBRef, NBA.com

This morning I won my 8th consecutive game of blitz on Lichess, equalling my previous 8 game record from Oct 2018. I was curious to see what had changed in my play over that period, as my elo has also gone up around 400 points in those years. As I'd done the numbers I thought I would share here - I'd love to hear your thoughts, answer any questions and hear any comparisons with yourselves.

My main takeaways from this:

• I've improved my accuracy (measured by ACL) by 29%, particularly noticeable with the reduction in blunders. Bar one messy game which had 7 blunders, the recent streak had a maximum of 2 blunders in each game. So often in 2018 I was missing/giving up free pieces, but so was my opponent. Now I'm about 400 points higher I'm typically avoiding those obvious blunders, and also spotting more tactics.
• At the higher ratings opponents tend to resign early, whereas in lower rated games you can expect to be forced to give checkmate. Due to this games are likely to be shorter at higher ratings (although I imagine they'd then get longer again at ratings higher than mine as blunders become even rarer).
• Playing with increment reduces the risk of losing on time in a winning position which happened twice to my opponents in 2018.
• I may have significantly improved, but I still have a long way to go! 1600 blitz on Lichess is pretty close to the median, which I'm pleased with but definitely still see room for major improvement. Going through my recent games showed me that my main flaw is missing longer tactics, so that's something I need to train. My long-term goal is to reach 2000.

Obviously there's a huge disclaimer about this being a very small sample size, but I thought it was interesting and hope you get something out of it. Feel free to let me know your thoughts in the comments, along with any other stat suggestions. I'd be interested to do a comparison vs an 8 game Magnus win streak at a similar format to see just how far off I am!

Have a great Christmas period :)

For example, despite the massive difference in landmass, Denmark has a population of ~270.000 more than Norway has (5.771.672 vs 5.501.167)

Anybody in charge of keeping a heterogeneous server landscape in good shape knows the headaches that come from having to use multiple packaging systems and repositories on Unix-like systems. This was covered in the previous article on Gemini [substitute https:// for gemini:// -- the link editor considers the correct URL invalid] and on the Web.

Today the second article on cross-platform package management has been published. It features a short description of what Pkgsrc and Ravenports are and a longer part on how they compare. The test environment and procedure is covered and of course the results are presented. At the end a conclusion is drawn.

The topic is a technical one, of course. But as usual I tried to make it more fun to read, writing it in blog-style language that isn't to stiff.

You can read the article here (Gemini) [substitute https:// for gemini://] or here (WWW).

Here i've tried to Compare some of the Best ODI bowlers with their teammates and see how better they were than their teammates. For this exercise i've considered List of Bowlers who have taken 150+ ODI Wickets and Average less than 30 Against the Top 8 ODI Teams.

I've compared the Bowling, Average, Bowling Strike Rate and Economy of the Player with their teammates's and mentioned how much better they were.

Following is the list sorted by the Bowling Average Differential.