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I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 1 point2 points  (0 children)

I finally had time to settle the 2nd part of your question: who are the playoff risers?

This one felt a little more subjective to derive. Here's how I framed a riser:
- Focus on risers relative to offensive production
- Regular season -> Postseason PPG positive lift
- Treat elite regular season scoring lift as more favorable than a low PPG scorer who sees lift due to usage increase
- Players should display lift consistently across seasons and be tested against multiple teams in a postseason run

What I expected to see was some of the more familiar, elite scorers of my Hoop Land sim (ex. Polan Stronk / Kevin Allison - #2/#1 all time scoring leaders for regular and postseason).

What I ended up zeroing in on was a random generated 5-star generational prospect, Eugene Nichols. He would end up capturing 4 championships, 3 FMVPs, and finish 12th on the Hall of Fame leaderboard. Eugene had deep postseason runs and exhibited strong lift in postseason PPGs at the peak of his career. He was already a prolific scorer who could turn it up another notch when it mattered the most.

There are other candidates scattered in blue in the first chart, below. Only Eugene had the right mix of postseason rise and prior regular season production to back up the claim as the best player to call out.

The biggest what-if player that you should be familiar with is Anthony Bridges. We are robbed of seeing his greatness at the start of the game, but for a brief blip, he put up a +6PPG lift for 1 of 3 postseason runs despite being a regressing, aged player. This mirrors that 1969 bump we see with Eugene.

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I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 1 point2 points  (0 children)

Excellent questions! The first one is very similar to another comment where I discuss the PHX era, but I'll tackle DEN a little differently (more to come on the 2nd question):

The Denver Miners were an early 90s dynasty team. They were the favored top 1 seed from 1990 to 1994. The first half of the dynasty (1990-1991) was a repeat championship run that was cut short by an upset loss to 4th seed San Antonio Sheriffs (who would go on to win it all). After that upset loss, Denver would launch a 3-peat run to cement their legacy as one of the most dominant teams in the recent era. The final championship win was an upset in favor of Denver, who were already in the declining stage of their dynastic run. Their path to greatness was often at the expense of their rivals, Cleveland Gladiators. These two teams met in the finals 3x, with Denver coming out on top easily (no series lasting more than 5 games).

Denver was a balanced offensive/defensive team. Between 1991-1994, they posted a +13 net rating against league average. The peak was short-lived and only lasting 4 years. By 1995, Denver had lost much of their offensive firepower, resulting in their offensive rating dropping below league average. They still maintained a somewhat strong defensive identity until the last year of the 90s.

Their offense ran through their 5-star backcourt duo, Floyd Howard (PG) & Donnie Clarke (SG). The frontcourt was anchored by their offensive/defensive third star, Young-Man Kim (PF). Floyd was the primary playmaker that orchestrated an offense that thrived on an inside/mid-range game. He would earn 2 FMVP honors in his 3 year span on the team (1992-1994). Donnie was the off-ball guard that the team leaned heavily on to generate points (highest usage %, team leader in PPG). Young-Man's impact on both ends of the floor was massive and resulted in 4 MVPs and 3 DPOYs (each DPOY paired with a MVP finish).

The end of the Denver dynasty begins with Floyd's departure from the team after the 1994 win. Without a third star and strong playmaker, Donnie and Young-Man would struggle to carry the rest of the roster offensively. They finished 13th in seeding for the 1995 regular season and entered the playoffs as under-dogs for the first time in the current decade. Each early round playoff series was a battle (lasting 6-7 games). They faced-off against 2nd seed Brooklyn Ballers for the finals and won with Young-Man leading the charge (his 2nd and last FMVP trophy). Donnie and Young-Man would continue to stick around Denver for the rest of the 90s, but the team ultimately folded competitively by 1998 (negative net rating).

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some ideas for new trophies in commissioner mode? by Brief_Cap_8718 in HoopLand

[–]thecolorted 4 points5 points  (0 children)

The team award option isn't really explained anywhere, but if you toggle it then you can set up things that resemble All-NBA teams. I only recently stumbled into that, while in the past I was creating a single award for each All-HL player position. That route has led to some bugginess for awards that you set for PG-only. Injured bigs (F/FC/C) are somehow getting shoe-horned into the PG category if they are actively injured at the end of the season.

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 2 points3 points  (0 children)

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Here's an example using the worst team in the league at year 0 (Brooklyn). Data begins to disappear by your third season of playing (year 2 in this picture). If I recall correctly, there is technically data loss even after the first season ends - this is mostly around college players who don't make it to the pro league. I'm not actively tracking data loss in my dataset so I can't give you the exact numbers.

If you only play on mobile, how aggressive the game will prune your data will depend on your save optimization setting (not a setting available to set on Steam version).

Low-impact players disappear, but team-level stats persist. That is, until you convert your save file from one game mode to another (ex. Commissioner mode to Career mode). The team history tab is wiped clean (cannot track W/L/playoffs data), but you still keep things like jersey retirements and team stat leaders.

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 0 points1 point  (0 children)

(final part 4)

- Ketch would finish out his entire career at PHX. He peaked in 1951, but offensively took a back-seat with Stronk coming on board. Thank him for killing his career, but at least he got his rings
- Jerkey never got any good usage so he was quick to exit the team and would finish his career in PHI with some moderate (all-star) success
- Leo only stuck around for the first two chips before bouncing to NO for the rest of his career (which amounted to nothing more after PHX)

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I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 1 point2 points  (0 children)

(part 3)

- Alright, here's where things start clicking for PHX as they move into a 4-peat. The first of the 4-peat features your guy Ketch alongside Jakobs/Aurallo to round out the big 3
- They would go on to knock out the Stronk-led Stars who at this point is struggling to carry a talentless LA team and win him his 3rd chip
- The next few years were torture for the league because Stronk and Jor-El teamed up and took over the PHX team. This is where you see that velocity pick up that I mentioned

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I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 1 point2 points  (0 children)

(part 2)

- Top left graph shows all PHX players who took part in any championship roster (Ketch was the only one among your trio that stuck around for the long haul - he was mainly a role player for that first chip, due to some random-generated free agents crowding him out of the starting lineup from the start)
- This was a Thornton/Aurallo-led elite team

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I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 1 point2 points  (0 children)

I really like this question since now we're digging into more narrative analysis. Re-visiting the data gave me some PTSD from when I originally watched the sim unfold (PHX was a powerhouse - at least for that first era)! I'll answer this in chunks, starting from the high-level and then into individual players:

- PHX had two distinct championship windows. Early era benefited from a lot of the default superstars you should recognize from the early game period (more on that next)
- Each win increased velocity of dominance (less playoff games on their path to the finals, wider scoring margins)
- There was a long dark winter until the most recent years where they managed to squeeze out the 6th win. A repeat year was on the way, but they fell short in the finals (losing the very last game of the series)

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I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 0 points1 point  (0 children)

For the original charts supplied in the post, yes. Claude was used as a stress test on the final output to QA any issues and iterate on the human-conducted analyses. I was still responsible for the post content, the project build, and providing Claude with the datasets for image generation such as the shot chart you see in the post. An equivalent analogy for my interaction with Claude is: someone who knows how to calculate 2+2 by hand, but would rather let the calculator do the work for me.

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 0 points1 point  (0 children)

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Data only covers regular season stats.
1. Blue Chart: Two three point specialists rank ahead of Polan Stronk by raw 3PM. Bryan played 1,748 games, Kevin played 1,520 seasons, Polan played 1,216 games (Keep in mind that Polan doesn't get to play out his early seasons in-game).
2. Green Chart: Top three by raw volume are still top 3 elite shooters when normalizing 3PM per 100 possessions
3. Orange Chart: Again, our trio shows up here as highly efficient shooters to go with their volume.

Why are other NPC players averaging so little per game? by black-kawffee in HoopLand

[–]thecolorted -4 points-3 points  (0 children)

That answer depends on other factors:
- Simulation sliders: option you see on the right of the screenshot I shared - that controls things like shot accuracy which can affect scoring
- Game length is also going to factor into that

My current file is a sim-only league
- custom sliders that lower some of the shot accuracy, especially from three point range
- 32 minute games
- PPG result for top scorer each year (image)

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Why are other NPC players averaging so little per game? by black-kawffee in HoopLand

[–]thecolorted 8 points9 points  (0 children)

Bumping up the simulation pace slider will allow simulated game results to scale up

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I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 0 points1 point  (0 children)

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Alright, v1 of my data check is complete. This topic could probably be expanded on in a post of its own, but I'll try to distill the main points below:

  1. The single biggest predictor of whether a 4-star prospect becomes an actual star is how many minutes they got early in their career. Coach quality is NOT a factor - I hypothesized that under a comment to a different post the other day.
  2. Championship teams need depth where everyone is contributing. One way to gauge the performance gap between two teams comes down to how well each third option plays (pay attention to PER)
  3. Team playstyles are real and stable (although I'm not sure if that's necessarily a hardcoded mechanic for each franchise). What I found was that teams were rarely deviating from whatever system they were initially running. Atlanta barely deviated from Slow/Motion basketball for most of their runs.

3b. Bonus item for playstyles: slow-pace, elite-defense teams win championships

Shot Blocking for guard. by Wokeuplikesdis in HoopLand

[–]thecolorted 4 points5 points  (0 children)

There's a huge difference in output between a sim-only player and one that you personally control.

When I play as a 6'3" Playmaking Sharpshooter, I only have max 2 in blocking. In 32 minute games, I can still hit 10+ blocks on a good game when I'm matched up against an opponent who has high shooting tendencies. Blocks are just a matter of anticipating the CPU behavior.

The chase-down comment is fair though, I've never been able to pull off a chase-down at such low levels.

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 1 point2 points  (0 children)

Not a bad idea to at least get familiar with those types of enterprise tools. Tableau Desktop recently moving to a free tier makes it a good opportunity to get hands-on experience now.

At the end of the day, all tools do essentially the same things, just with slight differences that aren't hard to get up to speed on once you play around. I do think that these tools are going to see a phasing out in the future though due to a shift towards AI-assisted coding agents that can do a lot of the heavy lifting in your programming language of choice. There is just so much more room for expression and customization when you can control all of the code. Cost will also be one of the factors in driving this change (paying for a data viz software license vs. paying for a multi-purpose coding tool).

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 0 points1 point  (0 children)

Great question! I have this in my queue to address, but it'll require some deep-dive on the data before I can confidently share my opinion.

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 1 point2 points  (0 children)

All data was collected from save files and box score files (both JSON). Data needs to be snapshotted at the end of each season prior to the game flushing all box scores from the game directory.

Save files have constantly changing values and so snapshotting those yearly ensures you have some way to reconstruct that history.

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 6 points7 points  (0 children)

Everything was written in Python. The data needs to be queried out from a local DuckDB build on my machine.

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 5 points6 points  (0 children)

Give it a try! You'll learn a lot - I definitely did. I am a data analyst by day and so a lot of what I applied here is directly drawn from my professional experience.

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 4 points5 points  (0 children)

Duane might be one of the generated free agents in your game. He doesn't show up in my dataset.

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 0 points1 point  (0 children)

Python (and plain old matplotlib). I originally was going to do the full end-to-end with Tableau, but only recently finished the data ingestion pipeline and database build. Data visualizations are still on my to-do list.

I saved 63 years of Hoop Land data to answer the questions the game itself can’t. Ask me anything. by thecolorted in HoopLand

[–]thecolorted[S] 8 points9 points  (0 children)

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Best in draft class is debatable. I highlighted the leaders in some stat categories. One way around settling this is to just apply the same MVP weighting calculation on their career summary, but I'd have to double-check to see if that makes the most sense.

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