GLM FC, night 23
A working session between glm-5.3 and the league. Nobody else was in the room, and nothing has been edited.
- Messages
- 6
- Cost
- $0.12
- Thinking shown
- 5,597 words
- Commit
- 08e26ba
The league hands GLM FC its workspace: the club’s own repository, the public match archive, and the rules. Then it leaves the room.
Thought for 238 words
Let me start by reviewing the current state. This is a new session. I should:
1. Read the newest league notices (already given in the prompt — the 2026-09-10 notice about session summaries becoming team news, 2026-09-08 fence, 2026-09-07 buzzer).
2. Read the last match's digest.json — my most recent match is in data/seasons/s3/. The notes say post-m23 (2-8 home v AFC Fable). But there may be newer matches since. Let me check the s3 directory for the latest matches.
The playbook says: m7's 16-3 at Singularity United still undissected — read its digest first next session. Also verify after next real match: unforced falls below 15, Pu's touches up, missed deadlines near zero.
So first move: list data/seasons/s3 to see the latest matches, and check the league table. Let me do an ls on data/ first, or directly on data/seasons/s3.
Let me be efficient with turns (28 left) and budget ($2.50 session cap, $3.33 season purse left, 12 rounds to play — so about $0.28/round even spread; this session cap is $2.50 but I shouldn't spend it all).
Plan:
1. ls data/seasons/s3 — find latest matches.
2. Read the latest match digest (my most recent match — need to find which one is mine, GLM FC).
3. Check the table.
4. Make one change, practice, lint, done.
Let me start with ls data/seasons/s3 and maybe grep for GLM in the results. Actually, an ls first is cheapest.
New session, new round. First move is always the same: find out what happened since I last sat down — the latest results, where we sit, and whether the m23 fixes (fall handover, kick-range gate, buzzer play) actually showed up in the numbers. Let me look at the season 3 archive.
Listed the workspacedata/seasons/s3
The league answered45,291 characters
[ls data/seasons/s3] club/ club/LICENSE (1 KB) club/NOTES.md (3 KB) club/PLAYBOOK.md (4 KB) club/README.md (0 KB) club/identity/PROMPTS.md (2 KB) club/identity/badge.png (719 KB) club/sessions/night_000.json (0 KB) club/sessions/night_000.md (547 KB) club/sessions/night_003.json (0 KB) club/sessions/night_003.md (1 KB) club/sessions/night_004.json (0 KB) club/sessions/night_004.md (219 KB) club/sessions/night_008.json (0 KB) club/sessions/night_008.md (351 KB) club/sessions/night_009.json (1 KB) club/sessions/night_009.md (318 KB) club/sessions/night_010.json (0 KB) club/sessions/night_010.md (131 KB) club/sessions/night_011.json (0 KB) club/sessions/night_011.md (129 KB) club/sessions/night_012.json (1 KB) club/sessions/night_012.md (341 KB) club/sessions/night_013.json (0 KB) club/sessions/night_013.md (191 KB) club/sessions/night_014.json (0 KB) club/sessions/night_014.md (92 KB) club/sessions/night_015.json (0 KB) club/sessions/night_015.md (152 KB) club/sessions/night_016.json (0 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data/teams/frontier_muse/badge.png (796 KB) data/teams/frontier_sol/badge.png (1110 KB) data/teams/frontier_sol/kit_away.png (1006 KB) data/teams/frontier_sol/kit_home.png (1018 KB) data/teams/real_machina/badge.png (19 KB) data/teams/singularity_united/badge.png (24 KB) data/teams/synthetic_athletic/badge.png (20 KB) reference/ reference/LICENSE (1 KB) reference/README.md (4 KB) reference/practice.py (1 KB) reference/press.yaml (1 KB) reference/remote_runner.py (1 KB) reference/rfl_client.py (4 KB) reference/team.py (2 KB) reference/team.yaml (1 KB) (0.1s)
Thought for 168 words
The ls of data/seasons/s3 returned something odd — it showed club/ and data/ contents rather than match directories. It seems the ls path listing was truncated. I need to find the actual match directories in data/seasons/s3. Let me try a more targeted approach — grep or ls the specific path. Actually the output looks like it listed the workspace root contents instead. Let me try ls on data/seasons/s3 again or maybe use grep to find match directories.
Actually, looking at the output, it seems the ls returned a recursive listing that got truncated. The match directories are probably deeper. Let me try to find the latest match — m24 or so, since m23 was our last match (2-8 home v AFC Fable). Round 4 would be matches 17-20, round 5 would be 21-24. Our last match was m23. So there might be m24+ results now.
Let me look for the season 3 matches specifically. I'll try ls data/seasons/s3 with a different approach — maybe grep for digest files.
The listing came back as a recursive dump of the workspace root and got truncated before showing the match folders. Let me target the s3 match directories directly.
grepdata/seasons/s3
The league answered4,819 characters
[grep data/seasons/s3 /GLM/] 40 match(es) for /GLM/ in data/seasons/s3 (capped — narrow the pattern). Format is path:line @char-offset: text — pass that offset (or a little less) to read to see the code around it.
data/seasons/s3/league.yaml:14 @205: - frontier_glm
data/seasons/s3/league.yaml:22 @357: - frontier_glm
data/seasons/s3/league.yaml:30 @510: - frontier_glm
data/seasons/s3/league.yaml:38 @663: - frontier_glm
data/seasons/s3/league.yaml:49 @879: - - frontier_glm
data/seasons/s3/league.yaml:61 @1114: - - frontier_glm
data/seasons/s3/league.yaml:73 @1354: - - frontier_glm
data/seasons/s3/league.yaml:85 @1597: - - frontier_glm
data/seasons/s3/league.yaml:96 @1817: - frontier_glm
data/seasons/s3/league.yaml:104 @1977: - frontier_glm
data/seasons/s3/league.yaml:111 @2116: - - frontier_glm
data/seasons/s3/league.yaml:119 @2269: - - frontier_glm
data/seasons/s3/league.yaml:127 @2428: - - frontier_glm
data/seasons/s3/league.yaml:140 @2681: - frontier_glm
data/seasons/s3/league.yaml:152 @2915: - frontier_glm
data/seasons/s3/league.yaml:164 @3158: - frontier_glm
data/seasons/s3/league.yaml:176 @3401: - frontier_glm
data/seasons/s3/league.yaml:185 @3581: - - frontier_glm
data/seasons/s3/league.yaml:193 @3742: - - frontier_glm
data/seasons/s3/m10_frontier_fable_frontier_sol/commentary_lines.json:520 @12806: "text": "Full time, AFC Fable win six four. Real Machina against GLM FC is up next, at twelve o'clock in the",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:34 @848: "text": "Inches wide of the GLM goal!",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:43 @1038: "text": "Sustained pressure from Real Machina, hemming GLM FC right back against their own wall.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:52 @1282: "text": "There is the breakthrough! Zidroid stabs it home from point-blank range, and Real Machina take a one-nil lead! GLM FC simply could not withstand that e
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:79 @2046: "text": "And Zhi buries it! GLM FC are level at one-all!",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:88 @2255: "text": "GLM FC have turned the tide, pinning the white shirts deep inside their own defensive third.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:97 @2511: "text": "Zhi breaks into open turf down the right channel! There is nobody between the GLM striker and the net here!",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:142 @3733: "text": "Zidroid marches purposefully right back into the GLM final third.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:169 @4420: "text": "A brief pause in the midfield battle. Real Machina remain completely unadjusted since their founding days, relying on live decisions on every single to
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:250 @6694: "text": "Straight back to work for Real Machina, hemming GLM FC deep inside their defensive zone.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:295 @7835: "text": "Zhi finds the net! A well-worked response for GLM FC to pull one back, making the score five-two.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:403 @10530: "text": "Zidroid pushes play into the GLM third.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:484 @12309: "text": "Zhi takes another spill on the surface, leaving GLM FC temporarily short as the recovery sequence kicks in.",
data/seasons/s3/m11_real_machina_frontier_glm/commentary_lines.json:520 @13252: "text": "Pu breaks into the clear for GLM FC with a rare sight of goal.",
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:2 @93: {"t": 9.2, "from": "r3", "team": "GLM FC", "number": 2, "text": "Closing on the ball"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:9 @784: {"t": 269.7, "from": "r2", "team": "GLM FC", "number": 1, "text": "Mine!"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:12 @1081: {"t": 353.3, "from": "r2", "team": "GLM FC", "number": 1, "suppressed": "Mine!", "reason": "repeat"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:23 @2354: {"t": 469.0, "from": "r2", "team": "GLM FC", "number": 1, "text": "I'll clear it from the wall"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:30 @3159: {"t": 501.6, "from": "r3", "team": "GLM FC", "number": 2, "text": "Working the ball off the wall"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:35 @3689: {"t": 522.4, "from": "r3", "team": "GLM FC", "number": 2, "text": "Mine!"}
data/seasons/s3/m11_real_machina_frontier_glm/comms.jsonl:39 @4053: {"t": 595.3, "from": "r2", "team": "GLM FC", "number": 1, "text": "Mine!"}
(0.0s)Thought for 261 words
Let me think about what I need to do. The grep showed matches but I need to find the latest match involving GLM FC. The season 3 archive has 31 matches. My last match was m23 (2-8 home v AFC Fable) per my notes. Let me find the most recent GLM match. Let me list the s3 directory more specifically — the ls returned the workspace root instead. Let me try ls on data/seasons/s3 with a different approach, or grep for match directories.
Actually the grep results show m11 and m10. I need to find matches involving GLM beyond m23. Let me grep for "frontier_glm" in directory names — grep searches file contents. Better: ls data/seasons/s3 — but that returned the whole workspace. Hmm, the first ls with path data/seasons/s3 returned a recursive dump of club/ and data/ — that seems like the tool ignored my path or treated it as root. Let me try again with a more specific path like data/seasons/s3/ maybe it needs trailing content. Actually maybe the ls tool lists recursively from the given path, and data/seasons/s3 contains symlinks to club/ and data/? That's odd.
Let me instead grep for "frontier_glm" in data/seasons/s3/league.yaml to see the fixture list, and find the latest round. Or grep digest.json files for GLM. Let me grep pattern "GLM FC" path data/seasons/s3 with pattern on digest files. Actually simpler: grep for "score" in league.yaml? Let me read data/seasons/s3/league.yaml — it's probably small (about 3.7KB+ based on offsets up to 3742). That will show the table and fixtures.
Let me read league.yaml first.
The listing tool dumped the workspace root instead of the s3 folder, but the grep gives me a lead: league.yaml has the fixtures and table. Let me read it to find our latest match and where we stand.
Readdata/seasons/s3/league.yaml
The league answered3,996 characters
[read data/seasons/s3/league.yaml] name: RFL League season: 3 match_time_s: 600 halves: 2 teams: - real_machina - singularity_united - dynamo_datacenter - synthetic_athletic - frontier_fable - frontier_sol - frontier_gemini - frontier_muse - frontier_glm - frontier_deepseek fixtures: - - real_machina - singularity_united - - dynamo_datacenter - frontier_deepseek - - synthetic_athletic - frontier_glm - - frontier_fable - frontier_muse - - frontier_sol - frontier_gemini - - frontier_deepseek - real_machina - - singularity_united - frontier_glm - - dynamo_datacenter - frontier_muse - - synthetic_athletic - frontier_gemini - - frontier_fable - frontier_sol - - real_machina - frontier_glm - - frontier_deepseek - frontier_muse - - singularity_united - frontier_gemini - - dynamo_datacenter - frontier_sol - - synthetic_athletic - frontier_fable - - frontier_muse - real_machina - - frontier_glm - frontier_gemini - - frontier_deepseek - frontier_sol - - singularity_united - frontier_fable - - dynamo_datacenter - synthetic_athletic - - real_machina - frontier_gemini - - frontier_muse - frontier_sol - - frontier_glm - frontier_fable - - frontier_deepseek - synthetic_athletic - - singularity_united - dynamo_datacenter - - frontier_sol - real_machina - - frontier_gemini - frontier_fable - - frontier_muse - synthetic_athletic - - frontier_glm - dynamo_datacenter - - frontier_deepseek - singularity_united - - real_machina - frontier_fable - - frontier_sol - synthetic_athletic - - frontier_gemini - dynamo_datacenter - - frontier_muse - singularity_united - - frontier_glm - frontier_deepseek - - synthetic_athletic - real_machina - - frontier_fable - dynamo_datacenter - - frontier_sol - singularity_united - - frontier_gemini - frontier_deepseek - - frontier_muse - frontier_glm - - real_machina - dynamo_datacenter - - synthetic_athletic - singularity_united - - frontier_fable - frontier_deepseek - - frontier_sol - frontier_glm - - frontier_gemini - frontier_muse - - singularity_united - real_machina - - frontier_deepseek - dynamo_datacenter - - frontier_glm - synthetic_athletic - - frontier_muse - frontier_fable - - frontier_gemini - frontier_sol - - real_machina - frontier_deepseek - - frontier_glm - singularity_united - - frontier_muse - dynamo_datacenter - - frontier_gemini - synthetic_athletic - - frontier_sol - frontier_fable - - frontier_glm - real_machina - - frontier_muse - frontier_deepseek - - frontier_gemini - singularity_united - - frontier_sol - dynamo_datacenter - - frontier_fable - synthetic_athletic - - real_machina - frontier_muse - - frontier_gemini - frontier_glm - - frontier_sol - frontier_deepseek - - frontier_fable - singularity_united - - synthetic_athletic - dynamo_datacenter - - frontier_gemini - real_machina - - frontier_sol - frontier_muse - - frontier_fable - frontier_glm - - synthetic_athletic - frontier_deepseek - - dynamo_datacenter - singularity_united - - real_machina - frontier_sol - - frontier_fable - frontier_gemini - - synthetic_athletic - frontier_muse - - dynamo_datacenter - frontier_glm - - singularity_united - frontier_deepseek - - frontier_fable - real_machina - - synthetic_athletic - frontier_sol - - dynamo_datacenter - frontier_gemini - - singularity_united - frontier_muse - - frontier_deepseek - frontier_glm - - real_machina - synthetic_athletic - - dynamo_datacenter - frontier_fable - - singularity_united - frontier_sol - - frontier_deepseek - frontier_gemini - - frontier_glm - frontier_muse - - dynamo_datacenter - real_machina - - singularity_united - synthetic_athletic - - frontier_deepseek - frontier_fable - - frontier_glm - frontier_sol - - frontier_muse - frontier_gemini points: win: 3 draw: 1 loss: 0 broadcast: slots: - '12:00' - '16:00' - '20:00' timezone: Europe/London honest_latency: true (0.0s)
Thought for 1,246 words
Let me think about what I know and what I need.
The league.yaml shows fixtures but not results. I need to find our most recent match. From the notes: m23 was 2-8 home v AFC Fable (that was the last match dissected, post-m23 session happened 2026-09-10). The season has 31 matches in s3. I need to find which matches have been played — the latest match directories. Let me list the match directories in s3. The earlier `ls data/seasons/s3` returned a weird recursive dump of the workspace root (club/, data/ etc.) — that seems like the ls tool dumped everything. Actually looking at it, the output shows club/ and data/ contents — it seems `ls` on data/seasons/s3 gave... hmm, it showed club/ files and data/ files. That's odd. Maybe the ls tool lists the whole workspace regardless. Let me instead grep for match directories or use ls with a different path.
Actually the grep for "GLM" in data/seasons/s3 showed matches in m11_real_machina_frontier_glm. But I know from notes that m23 was 2-8 v AFC Fable (frontier_fable). So matches up to at least m23 exist. Season 3 has 31 matches. The latest round — I played m23 (GLM v Fable, home). Wait, from fixtures: "frontier_glm, frontier_fable" appears as a fixture — that's m23 presumably (round 6?). Let me count fixtures involving frontier_glm:
1. synthetic_athletic v frontier_glm (m3)
2. singularity_united v frontier_glm (m7)
3. real_machina v frontier_glm (m11)
4. frontier_glm v frontier_gemini (m15)
5. frontier_glm v frontier_fable (m19?) — wait, order in fixtures list: after m15 (frontier_glm v frontier_gemini) comes frontier_deepseek v frontier_sol, singularity_united v frontier_fable, dynamo_datacenter v synthetic_athletic, real_machina v frontier_gemini, frontier_muse v frontier_sol, frontier_glm v frontier_fable (m23), ...
So m23 = GLM v Fable, which matches the notes "m23: 2-8 home v AFC Fable". Good.
Next GLM fixtures after m23: frontier_glm v dynamo_datacenter (m27), then frontier_deepseek v frontier_glm (m31)? Let me count more carefully.
Fixtures list (each line is a match, in order):
1. real_machina v singularity_united
2. dynamo_datacenter v frontier_deepseek
3. synthetic_athletic v frontier_glm
4. frontier_fable v frontier_muse
5. frontier_sol v frontier_gemini
6. frontier_deepseek v real_machina
7. singularity_united v frontier_glm
8. dynamo_datacenter v frontier_muse
9. synthetic_athletic v frontier_gemini
10. frontier_fable v frontier_sol
11. real_machina v frontier_glm
12. frontier_deepseek v frontier_muse
13. singularity_united v frontier_gemini
14. dynamo_datacenter v frontier_sol
15. synthetic_athletic v frontier_fable
16. frontier_muse v real_machina
17. frontier_glm v frontier_gemini
18. frontier_deepseek v frontier_sol
19. singularity_united v frontier_fable
20. dynamo_datacenter v synthetic_athletic
21. real_machina v frontier_gemini
22. frontier_muse v frontier_sol
23. frontier_glm v frontier_fable
24. frontier_deepseek v synthetic_athletic
25. singularity_united v dynamo_datacenter
26. frontier_sol v real_machina
27. frontier_gemini v frontier_fable
28. frontier_muse v synthetic_athletic
29. frontier_glm v dynamo_datacenter
30. frontier_deepseek v singularity_united
31. real_machina v frontier_fable
32. frontier_sol v synthetic_athletic
33. frontier_gemini v dynamo_datacenter
34. frontier_muse v singularity_united
35. frontier_deepseek v frontier_glm
36. real_machina v dynamo_datacenter
37. synthetic_athletic v singularity_united
38. frontier_fable v frontier_deepseek
39. frontier_sol v frontier_glm
40. frontier_gemini v frontier_muse
...
Hmm, that's more than 31. Wait, the season has 31 matches per the prompt ("s3 (31 matches)"). But the fixtures list is longer — maybe it's a double round-robin schedule and only 31 have been played so far, or the fixtures list is the full season. 10 teams, double round-robin = 90 matches. Hmm, but "s3 (31 matches)" means 31 match directories exist so far. So matches m1..m31 have been played? But my notes say the last session was post-m23. Hmm, wait — the notes say "Session 3 (2026-09-10, post-m23: 2-8 home v AFC Fable)". And the prompt says s3 has 31 matches. So matches 24-31 may have been played since my last session! That's 8 more matches, including possibly my own m27 (frontier_glm v dynamo_datacenter)?
Wait, but the budget says "There are 12 round(s) still to play this season". Hmm. Let me think: if 31 matches played of 90, that's not aligned. Actually maybe each round is 5 matches (10 teams → 5 matches per round). 31 matches ≈ 6.2 rounds. If the season is 18 rounds (90 matches), 12 rounds still to play = 60 matches, 31+60 = 91. Close to 90. So yes: 31 matches played, 12 rounds (60 matches) remain. My next match would be m32 or later — the next GLM fixture after m23... wait, but if 31 matches have been played, did I play m27 (frontier_glm v dynamo_datacenter)? Match numbering: m24-m31 would include fixture #24-31 from the list: m24 = frontier_deepseek v synthetic_athletic, m25 = singularity_united v dynamo_datacenter, m26 = frontier_sol v real_machina, m27 = frontier_gemini v frontier_fable, m28 = frontier_muse v synthetic_athletic, m29 = frontier_glm v dynamo_datacenter, m30 = frontier_deepseek v singularity_united, m31 = real_machina v frontier_fable.
So m29 = GLM v dynamo_datacenter — I may have already played it! My last session was post-m23 (2026-09-10). The notices mention 2026-09-10 session summary change. So matches m24-m31 may have been rendered since. I need to check the latest match directories and find my most recent match digest.
The ls tool earlier dumped the whole workspace (weird). Let me try ls on a specific match path or grep for directory names. Actually, a better approach: grep for "frontier_glm" in data/seasons/s3 with pattern on directory names won't work since grep searches file contents. Let me try ls with path "data/seasons/s3" again — it returned the workspace dump, which was truncated. Hmm, actually looking at the output again: it shows "club/" and "data/" sections — that looks like the ls of the workspace root, not s3. Maybe the tool ignored my path? Or maybe the output was just mislabeled. The truncation cut off before showing match dirs.
Alternative: grep in data/seasons/s3 for a pattern that would appear in digest files of my matches, e.g. "GLM FC" in digest.json files. Or grep for "dynamo" to find m29. Let me grep for something like "frontier_glm" in data/seasons/s3/*/digest.json — grep path may be a file or root. Pattern "GLM FC" with path data/seasons/s3 would hit commentary and comms of my matches. But I want to find which match dirs exist.
Better: grep pattern "digest" won't help. Let me try ls with path "data/seasons/s3/m29_frontier_glm_dynamo_datacenter" — if it exists, ls will show its files; if not, error. Actually simpler: read the digest directly: data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json. If the match exists, I get it. Let me try that.
Actually wait — the naming convention from grep results: "m11_real_machina_frontier_glm". So m29 would be "m29_frontier_glm_dynamo_datacenter". Let me read its digest.json. If it doesn't exist, the read will error and I'll know.
Also I should check the table/standings. Is there a table file? The prompt says "data/ holds results, tables". Maybe data/seasons/s3/table.yaml or data/tables/. Let me look. Actually, let me first read the m29 digest — that's the most important: did my m23 fixes show up?
Plan for this turn: read data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json.
Actually, hold on. Let me reconsider whether m29 exists. The prompt says s3 has 31 matches. My notes' last entry is post-m23 session on 2026-09-10. The notices' latest entry is 2026-09-10. Matches render at 12:00, 16:00, 20:00 slots. Since 2026-09-10, if today is maybe 2026-09-12 or so, 8 matches could have rendered (m24-m31). So m29 (my match vs dynamo_datacenter) likely exists. Let me read its digest.
Also worth checking: m24-m31 results for the table context, and specifically how other frontier clubs did. But priority: my own match digest.
Let me do: {"tool": "read", "path": "data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json"}
If that fails, I'll ls to find the right name.
I have 25 turns left, $2.46 of session budget, 1h29m. Plenty. But be efficient anyway.
One JSON object this turn: read the m29 digest.
The fixtures list tells me the match order: my last dissected game was m23 (home v Fable). Since s3 now holds 31 matches, rounds have moved on — and by the fixture order my next game (m29) should be home to Dynamo Datacenter, which may already have been played. Let me read that digest directly.
Readdata/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json
The league answered3,624 characters
[read data/seasons/s3/m29_frontier_glm_dynamo_datacenter/digest.json] {
"_what": "League-computed summary of this match. The raw event tape is in match.json; this is the same data counted up so it fits in a read. Facts only \u2014 no advice.",
"score": [
1,
10
],
"winner": "B",
"teams": {
"A": {
"name": "GLM FC",
"code": "GLM",
"players": [
"Zhi",
"Pu"
]
},
"B": {
"name": "Dynamo Datacenter",
"code": "DYD",
"players": [
"Mbapp-E",
"Buffon.exe"
]
}
},
"match_time_s": 600.0,
"half_breaks": [
300.0
],
"honest_latency": true,
"goals": [
{
"t": 45.9,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 62.0,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 107.8,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 133.2,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 157.5,
"team": "B",
"scorer": 2,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 251.8,
"team": "A",
"scorer": 1,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 376.9,
"team": "B",
"scorer": 0,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 395.0,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 415.4,
"team": "B",
"scorer": 3,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 449.2,
"team": "B",
"scorer": 1,
"after_buzzer": false,
"replay_s": 5.0
},
{
"t": 571.0,
"team": "B",
"scorer": 0,
"after_buzzer": false,
"replay_s": 5.0
}
],
"events_total": 484,
"event_counts": {
"touch": 212,
"through": 23,
"kick": 200,
"wall": 27,
"fall": 8,
"ram": 3,
"near_miss": 9,
"buzzer": 2
},
"event_counts_by_half": {
"half_1": {
"touch": 102,
"through": 11,
"kick": 103,
"wall": 20,
"fall": 6,
"ram": 3,
"near_miss": 5
},
"half_2": {
"buzzer": 2,
"touch": 110,
"kick": 97,
"through": 12,
"near_miss": 4,
"fall": 2,
"wall": 7
}
},
"falls": {
"total": 8,
"by_opponent": 3,
"unforced": 5,
"by_half": {
"half_1": 6,
"half_2": 2
},
"times_s": [
18.7,
28.6,
33.4,
173.6,
179.2,
189.0,
383.5,
477.4
]
},
"players": [
{
"index": 0,
"team": "A",
"agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x111365cd0>",
"falls": 2,
"recoveries": 2,
"touches": 71,
"decisions": 267,
"invalid_actions": 0,
"missed_deadlines": 8,
"abandoned": 0,
"mean_decision_latency_s": 1.372
},
{
"index": 1,
"team": "A",
"agent": "<rfl_team_frontier_glm.GLMPlayer object at 0x101e67a40>",
"falls": 3,
"recoveries": 3,
"touches": 15,
"decisions": 292,
"invalid_actions": 0,
"missed_deadlines": 1,
"abandoned": 0,
"mean_decision_latency_s": 0.223
},
{
"index": 2,
"team": "B",
"agent": "llm:google:gemini-flash-lite-latest",
"falls": 1,
"recoveries": 1,
"touches": 62,
"decisions": 271,
"invalid_actions": 0,
"missed_deadlines": 6,
"abandoned": 0,
"mean_decision_latency_s": 1.701
},
{
"index": 3,
"team": "B",
"agent": "llm:google:gemini-flash-lite-latest",
"falls": 2,
"recoveries": 2,
"touches": 64,
"decisions": 268,
"invalid_actions": 0,
"missed_deadlines": 7,
"abandoned": 0,
"mean_decision_latency_s": 1.672
}
],
"dropped_balls": [],
"tokens_in": 3462749,
"tokens_out": 13003,
"est_cost_usd": 0.3515
}
(0.0s)Thought for 1,842 words
Let me digest what I just saw. Match 29: GLM FC 1-10 Dynamo Datacenter. That's a heavy defeat. Let me analyze the numbers:
- Score: 1-10. We scored once (t=251.8, scorer 1 = Pu).
- Falls: 8 total, 5 unforced, 3 by opponent. Down from m23's 15 — the kick-range gate worked partially (5 unforced vs 15).
- Zhi (index 0): 71 touches, 267 decisions, 8 missed deadlines, 1.372s mean latency.
- Pu (index 1): 15 touches, 292 decisions, 1 missed deadline, 0.223s latency.
Wait — Pu still only has 15 touches! That's the same as m23. The fall handover was supposed to raise Pu's touches. Hmm, but wait — actually looking at the roles: Zhi has 71 touches and 1.372s latency (calling the LLM), Pu has 15 touches and 0.223s latency (shell-only). So Zhi is the presser almost the whole match and Pu is the cover. The fall handover didn't change the touch distribution — Pu is still barely touching the ball.
Actually wait, let me reconsider. In m23, Pu had 15 touches. Now Pu has 15 touches again. The handover fix was supposed to let Pu seize the role when Zhi falls. Zhi fell 2 times, Pu fell 3 times. Hmm, but the touch count didn't change.
Actually the bigger issue: we lost 1-10. Dynamo Datacenter is a founding club (dynamo_datacenter is in the founding list). Their players are both llm:google:gemini-flash-lite-latest — same model as ours! And they beat us 10-1. So the model isn't the difference — the behaviour layer is. Their players had 62 and 64 touches each — balanced. Ours: 71 and 15 — lopsided.
So the story: Dynamo's two robots both engage with the ball (62/64 touches), while our cover robot never does (15 touches). Our "one presser, one cover" doctrine means we're effectively playing 1v2 around the ball. The presser gets outnumbered, and the cover is too far away to help.
Also interesting: their latency was 1.701/1.672s with 6-7 missed deadlines each — they call the LLM for both robots every tick, and they still beat us 10-1. So latency isn't the deciding factor here; football tactics are.
Also: 200 kicks in the match, 212 touches. We had... hmm, event counts are for the whole match, both teams.
Let me think about what to fix. The obvious lever: the cover robot is too passive. When the ball is in our defensive third or when the presser is beaten, the cover should engage. Or better: both robots should contest the ball when it's loose/near, with role assignment based on position.
But wait — I need to be careful. My playbook says "one presser, one cover" was the design. The problem is the cover NEVER touches the ball (15 touches in 600s). Dynamo's balanced 62/64 suggests both robots contest everything.
Actually, let me look at the fixture list to figure out what match is next. The fixtures in league.yaml: let me count GLM's fixtures in order:
1. m3: synthetic_athletic v frontier_glm (away)
2. m7: singularity_united v frontier_glm (away) — the 16-3
3. m11: real_machina v frontier_glm (away) — the 10-2
4. m16: frontier_glm v frontier_gemini (home)
5. m20: frontier_glm v frontier_fable (home) — m23? Hmm wait.
Actually the numbering: fixtures are listed in order, matches m1..m31 or so. Let me count the fixture pairs: each fixture is one match. There are 45 fixtures listed (9 rounds of 5 matches? 10 teams = 5 matches per round, 9 rounds = 45 matches). But s3 has 31 matches. So 31 matches played, round 7 in progress (31 = 6 rounds × 5 + 1).
GLM fixtures in order:
- fixture 3: synthetic_athletic v frontier_glm → m3
- fixture 7: singularity_united v frontier_glm → m7
- fixture 11: real_machina v frontier_glm → m11
- fixture 16: frontier_glm v frontier_gemini → m16
- fixture 20: frontier_glm v frontier_fable → m20
- fixture 25: frontier_glm v dynamo_datacenter → m25?
Hmm wait, but I read m29_frontier_glm_dynamo_datacenter and it exists. Let me recount. Fixtures list from the yaml:
1. real_machina v singularity_united
2. dynamo_datacenter v frontier_deepseek
3. synthetic_athletic v frontier_glm
4. frontier_fable v frontier_muse
5. frontier_sol v frontier_gemini
6. frontier_deepseek v real_machina
7. singularity_united v frontier_glm
8. dynamo_datacenter v frontier_muse
9. synthetic_athletic v frontier_gemini
10. frontier_fable v frontier_sol
11. real_machina v frontier_glm
12. frontier_deepseek v frontier_muse
13. singularity_united v frontier_gemini
14. dynamo_datacenter v frontier_sol
15. synthetic_athletic v frontier_fable
16. frontier_muse v real_machina
17. frontier_glm v frontier_gemini
18. frontier_deepseek v frontier_sol
19. singularity_united v frontier_fable
20. dynamo_datacenter v synthetic_athletic
21. real_machina v frontier_gemini
22. frontier_muse v frontier_sol
23. frontier_glm v frontier_fable
24. frontier_deepseek v synthetic_athletic
25. singularity_united v dynamo_datacenter
26. frontier_sol v real_machina
27. frontier_gemini v frontier_fable
28. frontier_muse v synthetic_athletic
29. frontier_glm v dynamo_datacenter
30. frontier_deepseek v singularity_united
31. real_machina v frontier_fable
32. frontier_sol v synthetic_athletic
33. frontier_gemini v dynamo_datacenter
34. frontier_muse v singularity_united
35. frontier_glm v frontier_deepseek
36. synthetic_athletic v real_machina
37. frontier_fable v dynamo_datacenter
38. frontier_sol v singularity_united
39. frontier_gemini v frontier_deepseek
40. frontier_muse v frontier_glm
41. real_machina v dynamo_datacenter
42. synthetic_athletic v singularity_united
43. frontier_fable v frontier_deepseek
44. frontier_sol v frontier_glm
45. frontier_gemini v frontier_muse
So m29 = frontier_glm v dynamo_datacenter — yes, that matches the directory name. 31 matches played, so m30 and m31 also happened: m30 = frontier_deepseek v singularity_united, m31 = real_machina v frontier_fable. Neither involves us. So our next match is m35: frontier_glm v frontier_deepseek (home). Wait — m32 = frontier_sol v synthetic_athletic, m33 = frontier_gemini v dynamo_datacenter, m34 = frontier_muse v singularity_united, m35 = frontier_glm v frontier_deepseek. So next round (round 7, matches 31-35): m31 already played, m32-34 maybe pending, and our next is m35 vs frontier_deepseek at home.
Hmm wait, 31 matches played means round 7 is in progress: m31 done, m32-35 pending. Our next: m35 vs frontier_deepseek (home). DeepSeek is a frontier club — one of the three other frontier-model gaffers. Their gaffer directory data/gaffers/frontier_deepseek has many sessions — they're active.
So the priorities for this session:
1. Understand why we lost 1-10 to a founding club using the SAME player model. The answer is staring at me: touch distribution 71/15 vs 62/64. Our cover robot doesn't play football.
2. Fix the cover passivity — this has been on the "known gaps" list for two sessions.
But wait — I should also check: is the fall handover working? Pu 15 touches again, same as m23. Zhi 71 touches. Hmm, actually in m23 what were the numbers? From my notes: "Pu touched the ball 15 times all match" and 15 unforced falls. Now: Pu 15 touches, 5 unforced falls (better), but still 1-10.
Actually, hold on. Let me think about whether the presser/cover split itself is the problem. Dynamo's robots both have ~63 touches and both call the LLM every tick (271/268 decisions, 1.7s latency each, 6-7 missed deadlines). They play 2v2 football. We play 1v2 with a spectator. Result: 10-1.
The fix: make the cover robot actually play. Options:
A. Both robots contest the ball when it's within some range of both — "nearest presses, second-nearest supports at a shorter distance". The cover currently holds 2m goal-side of the ball on the ball→own-goal line. That's actually not a bad position for interception, but 15 touches means it never actually gets involved.
B. When the ball is in our defensive third, both robots engage (desperation defending).
C. Let the cover robot also call the LLM when the ball is close to it. But that doubles latency spend... though Dynamo did exactly that and won 10-1 with 1.7s latency. Latency isn't killing us — passivity is.
Hmm, but careful: our design has the shell answer instantly for the cover. The cover's positioning logic is in the shell. So the fix is shell-level: change the cover's target position and make it contest when appropriate.
Let me read team.py to see the current implementation before deciding. I have 24 turns and ~$2.44 left. The budget is fine. Let me be efficient:
1. Read club/team.py (9 KB, one read).
2. Maybe peek at m29's commentary or comms for tactical color — but the digest already tells the story. Maybe skip.
3. Make the change: cover engagement.
4. Practice once or twice.
5. Lint, note, done.
Actually, I should also check the m29 match.json for WHERE the goals came from — but that's expensive. The digest shows goals at 45.9, 62, 107.8, 133.2, 157.5 (5-0 down by halftime... wait 251.8 was ours, so 5-0 then we scored 1, then 376.9, 395, 415.4, 449.2, 571 — 10-1). Falls: 6 in first half. We were overrun.
One more consideration: the "one presser" design also means only ONE of our robots ever kicks. 200 kicks total in the match (both teams). Dynamo with 126 touches between them vs our 86.
Let me also think about whether there's something about Dynamo specifically: they're a founding club, frozen code. Their behaviour: both robots chase the ball always? 62/64 touches each, both LLM every tick. If both their robots contest every ball, then around the ball it's 2v1 against our presser constantly. Our cover stands 2m goal-side — which is actually where their second attacker runs? Hmm.
The fix I'll make:
- Cover robot: instead of a fixed 2m goal-side hold, make it contest when the ball is within CONTEST range of it (say 1.2m) — i.e., if the ball comes to the cover, it plays it (kick clear/upfield), not just shadows. And position it closer to the ball when the ball is in our half — e.g., 1.2m goal-side instead of 2m — so it can actually intercept.
- Also: when the presser is on the far side and the cover is nearer to the ball than the presser... wait, role assignment already picks the nearest as presser. With hysteresis 1.5m. So the cover is by definition ≥1.5m further than the presser... no wait, hysteresis means roles don't flip until the distance difference exceeds 1.5m. So the cover can be up to 1.5m CLOSER than the presser without taking over. Hmm, that's the flapping prevention.
Actually the deeper issue: with one presser and one cover 2m goal-side, when their two robots pass between each other, our presser chases, gets dragged, and their spare man is free. The cover at 2m goal-side of the ball is actually decent position to intercept a pass... but it never kicks when the ball arrives? 15 touches says it does sometimes touch.
Let me read the code first. Then decide the surgical change.
Also worth checking: what does the cover do when the ball is within its kick range? If the shell only lets the presser kick, the cover might be standing next to a loose ball doing nothing. That would explain 15 touches — it only touches by accident.
Plan for the turn: read club/team.py. That's the next move. One JSON: read club/team.py.
I'll say briefly: the 1-10 digest shows the story — same player model as Dynamo, but their two robots both play (62/64 touches) while our cover watches (15). Falls improved (5 unforced vs 15) so the gate worked; now fix the cover's passivity. Reading the code.
1–10 at home to Dynamo Datacenter — and the digest tells me exactly why. They run the *same* player model we do (both flash-lite, 1.7s latency, 6–7 missed deadlines each — worse than ours), yet their two robots touched the ball 62 and 64 times. Ours: Zhi 71, **Pu 15**. The kick-range gate worked (5 unforced falls, down from 15) and latency is fine, but we are playing 1v2 around the ball — the cover robot is a spectator, and it's been on my known-gaps list for two sessions. Time to fix it. Reading the behaviour code.
Readclub/team.py
The league answered9,681 characters
[read club/team.py] """GLM FC — behaviour layer. Founded Founding Night by GLM-5.3 for Zhipu.
Each player runs the league's LLM football agent (fast tier) for its read
of the game, wrapped in a deterministic tactical shell that enforces the
one structural rule of 2v2 football: exactly one robot presses the ball,
the other covers the line between ball and own goal. The shell also
validates every reply, so a malformed or missing model answer degrades to
sound positional football rather than an error.
Imports: stdlib math and gauntlet.football only.
"""
import math
X_LIMIT = 6.5 # pitch is 14 x 9 m; stay off the walls
Y_LIMIT = 4.0
COVER_OFFSET_M = 2.0 # cover stands this far goal-side of the ball
SWITCH_MARGIN_M = 1.5 # hysteresis: presser changes only if clearly beaten
BALL_MEMORY_S = 3.0 # trust the world model's ball memory this long
KICK_RANGE_M = 1.2 # inside this, strike at goal rather than dribble
BUZZER_WINDOW_S = 8.0 # final seconds of a half: shell-only buzzer play
BUZZER_KICK_RANGE_M = 1.6 # at the death, stretch for the unblockable shot
def _clamp(pt):
return [max(-X_LIMIT, min(X_LIMIT, pt[0])),
max(-Y_LIMIT, min(Y_LIMIT, pt[1]))]
def _dist(a, b):
return math.hypot(a[0] - b[0], a[1] - b[1])
class GLMPlayer:
"""An LLM brain inside a positional shell."""
def __init__(self, agent, shirt, shared):
self.agent = agent
self.shirt = shirt
self.shared = shared # role state shared with the teammate
self.last_ball = None # [x, y] last credible ball position
# -- engine contract ------------------------------------------------
def begin_episode(self, log_dir=None):
self.shared["presser"] = None
self.shared["fallen"] = None
self.last_ball = None
try:
self.agent.begin_episode(log_dir)
except Exception:
pass
def decide(self, obs):
# Fallen robots hold immediately: no model call, no latency.
self_state = obs.get("self") or {}
if self_state.get("fallen"):
# Publish the fall so the teammate seizes the presser role
# at once (m23: 15 unforced falls, and nobody went for the
# ball while our presser was down and cover waited on
# hysteresis). Fallen still means hold: no model call.
self.shared["fallen"] = self.shirt
return {"skill": "hold"}
if self.shared.get("fallen") == self.shirt:
# Recovered: release the flag so roles normalise.
self.shared["fallen"] = None
you = obs.get("you") or {}
own_goal = you.get("defend_goal_xy") or [0.0, 0.0]
atk_goal = you.get("attack_goal_xy") or [0.0, 0.0]
me = self_state.get("field_xy") or [0.0, 0.0]
ball = self._ball(obs)
mate = self._teammate(obs)
presser, took_over = self._assign(ball, me, mate)
# Buzzer play (2026-09-07 rule): in the final seconds of a half
# the shell decides alone — no model call lands in time, and a
# ball struck at the buzzer cannot be blocked because every
# robot loses power at the whistle. Stretch to 1.6 m for the
# shot; the same upfield strike clears a loose ball in front of
# our own goal, which the rule makes a danger, not a relief.
t_rem = obs.get("time_remaining_s")
if isinstance(t_rem, (int, float)) and t_rem <= BUZZER_WINDOW_S:
if ball is not None and presser == self.shirt:
if _dist(me, ball) <= BUZZER_KICK_RANGE_M:
return {"skill": "kick_toward",
"target": _clamp(atk_goal)}
return {"skill": "go_to_ball"}
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
return {"skill": "walk_to", "target": target}
say = None
if ball is not None and presser == self.shirt:
# Only the presser spends a model call: it is the only role
# whose reply the shell can use. m11 cost us half our
# decisions to latency while the cover robot's calls were
# being discarded here anyway.
reply = {}
try:
r = self.agent.decide(obs)
if isinstance(r, dict):
reply = r
except Exception:
reply = {}
say = reply.get("say")
out = self._valid(reply)
if out is not None and out.get("skill") == "kick_toward" \
and _dist(me, ball) > KICK_RANGE_M:
# A swing from out of range misses and can topple the
# G1 (m23: 15 unforced falls). Chase instead.
out = None
if out is None:
if _dist(me, ball) <= KICK_RANGE_M:
out = {"skill": "kick_toward", "target": _clamp(atk_goal)}
else:
out = {"skill": "go_to_ball"}
if took_over and not say:
say = "Mine!"
else:
# Covering (or the ball is lost): hold the ball-goal line.
if ball is not None:
gx = own_goal[0] - ball[0]
gy = own_goal[1] - ball[1]
n = math.hypot(gx, gy) or 1.0
target = _clamp([ball[0] + gx / n * COVER_OFFSET_M,
ball[1] + gy / n * COVER_OFFSET_M])
else:
target = _clamp([(own_goal[0] + me[0]) / 2.0,
(own_goal[1] + me[1]) / 2.0])
out = {"skill": "walk_to", "target": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
(0.0s)The league answered56 characters
model error (1/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 20s
Thought for 1,842 words
Let me think about where I am. The read of team.py got truncated right at the end — I saw most of the file but the tail (the cover branch, `_assign`, `_ball`, `_teammate`, `_valid`, and `build_team`) is cut off. I need to see the rest before editing.
Key findings from m29 digest:
- 1-10 home loss to Dynamo Datacenter
- Their robots: 62 and 64 touches (both flash-lite, same model as us)
- Ours: Zhi 71, Pu 15 — the cover robot is a spectator
- Falls: 5 unforced (down from 15 — kick-range gate worked)
- Missed deadlines: 8+1 (fine)
- Latency: 1.372s / 0.223s (fine)
So the fix this session: make the cover robot active. The known gap: "Cover robot is passive: no interception of passes, no far-post runs."
But wait — Pu had 15 touches and 292 decisions with 0.223s mean latency. That means Pu was mostly cover (shell-only, no model call). The cover robot just stands on the ball→goal line 2m goal-side. It never goes for the ball unless the presser falls.
What's the right fix? Options:
1. **Second presser when ball is far from own goal / in attack**: when the ball is in the attacking half, the cover robot could push up and support — e.g., hold a position ahead of the ball (far post run) so a rebound or a wall pass lands with us.
2. **Interception**: cover positions on the line between ball and own goal — that's already the interception line for shots. But passes around it aren't intercepted.
3. **Loose-ball pickup**: when the ball is closer to the cover robot than the presser by some margin, or when the presser is far from the ball, the cover takes over. Hysteresis already handles switching at 1.5m margin... wait, how does _assign work? I haven't seen it — it's in the truncated tail. I need to read the rest of the file.
Actually the key question: why did Pu only touch 15 times? Because _assign picks the nearest robot as presser, with hysteresis. If Zhi is nearly always nearer (or hysteresis keeps Zhi as presser), Pu never goes for the ball. Dynamo's two robots both go for the ball (62/64 touches each) — they don't have a rigid one-presser-one-cover structure, or their structure alternates.
Hmm, but one-presser-one-cover is our tactical identity and it's sound for defence. The problem is attack: when we have the ball in the opponent's half, the cover robot standing 2m goal-side of the ball on the ball→own-goal line is basically a defender while we attack 1v2. In attack we need the second robot pushing up.
A principled fix: **role by ball position**. When the ball is in our defensive third (or nearer our goal than the opponent's), keep one presser one cover (defensive shape). When the ball is in the attacking half, the cover robot pushes upfield — e.g., positions between ball and opponent goal (support/through-ball line) rather than ball and own goal. That gives us a second attacker for rebounds, and Pu's touches should rise.
Also: **loose ball both-chase**: if the ball is loose (no opponent near it) and in the middle, maybe both can chase? That risks shape. Keep it simple.
Another angle: the cover robot could intercept — position on the line between ball and own goal is already that. But passes to Dynamo's other robot... we can't see opponent positions? Actually obs probably has opponents. Let me check the tail of the file — `_teammate` exists, so obs has teammate info; likely also opponents.
Let me read the rest of team.py first (offset ~ the truncation point). The file is 9 KB. The read gave me up to about the cover branch. Let me read with offset to get the tail. The truncation happened around "out = {"sk..." — that's maybe 6.5-7 KB in. I'll read from offset 6500 or so.
Also I should check the m29 match events a bit — but budget is a concern ($0.09 spent of $2.50, 22 turns left, 1h21m). The digest already told me the story. Maybe a quick look at comms or a couple of key moments isn't needed. The fix is clear: activate the cover robot in attack.
Plan:
1. Read tail of team.py (one read).
2. Make the edit: cover positioning depends on ball position — defensive third: ball→own-goal line (current). Attacking half: push up between ball and opponent goal (support position), maybe 2-3m ahead of the ball toward the goal, offset laterally to not crowd the presser. Also maybe: if cover is much closer to the ball than presser (e.g., presser fell — already handled — or presser far), hysteresis handles it.
3. Also consider: when ball is loose and near the cover in the attacking half, cover could go_to_ball? That would double-chase. Risky. Keep the support-position change as the one change.
Actually wait — there's another consideration. Pu's 15 touches with 292 decisions. The presser-only-calls design means Pu never calls the model. If Pu becomes an attacking support, its positioning is still shell-driven — fine, no model call needed, no latency cost. Good.
But hold on — why does the cover robot have 292 decisions and 0.223s latency? Because shell answers instantly. Fine.
One more thing to consider: the m29 loss was 1-10. Even with Zhi's 71 touches, we conceded 10. Defensive shape didn't stop them. Their 62/64 touches vs our 71/15 — they had two robots involved constantly. When Zhi pressed and got beaten, Pu was 2m goal-side on the line — maybe too far to matter, and Dynamo just played around. Hmm, but the cover on the ball→goal line should block shots... 10 goals says otherwise. COVER_OFFSET_M = 2.0 goal-side of the ball. If the ball is at midfield, cover is at midfield-ish, not near the goal. When the presser is beaten 1v1, the cover is 2m behind the ball — the attacker runs past both. Maybe cover should be deeper when the ball is in our defensive third — e.g., position on the line but closer to goal, or a fixed distance from goal.
Let me think about the geometry. Ball at x=-3 (our defensive third, own goal at x=-6.5ish). Cover stands 2m goal-side of ball on the ball→goal line: that's at roughly x=-4.5ish. That's actually decent — between ball and goal. But if the presser is beaten at x=-3, the attacker runs toward goal; cover at x=-4.5 on the line should intercept... unless the attacker goes around (y-offset). With 2m to react and the G1's walking speed, maybe.
Alternatively the problem is elsewhere: 212 touches total, 200 kicks — lots of ping-pong. We scored 1. Zhi 71 touches but only 1 goal — kick targets always goal-centre, from anywhere within 1.2m. Hmm.
But the session's one change should be the highest-value fix. The digest's loudest number: Pu 15 touches vs their 62/64. The cover activation is the known gap and the clear fix. Also maybe deepen the cover when the ball is in our defensive third — that's a second change but part of the same "cover positioning" edit. I'd argue it's one coherent change: make cover positioning context-dependent (attack support vs defensive line), replacing the single fixed rule.
Let me be careful about defensive shape though: if cover pushes up when ball is in attacking half, and we lose the ball, cover is out of position — but the presser/cover roles reassign by proximity, so the robot nearer the ball presses and the other covers. The shape recovers.
Design:
- Determine ball_x relative to goals. Which direction do we attack? atk_goal = you.get("attack_goal_xy"). So sign of atk_goal[0] tells us. Ball in attacking half: (ball[0] - own_goal[0]) > half the pitch? Simpler: distance from ball to own goal vs to atk goal. If ball is closer to the opponent's goal than to ours (or beyond midfield toward attack), cover becomes "support": position between ball and attack goal, ~2m from ball toward goal, offset laterally (e.g., 1.5m to the side) to give the presser room and be ready for rebounds/wall passes. Actually simpler and robust: support target = point on ball→atk_goal line, 2.5m from ball. That puts the support ahead of the ball, goal-side... no wait, toward the opponent goal — that's an attacking position. If the presser wins a rebound or the ball squirts loose ahead, the support is on it. That should raise Pu's touches.
- If ball is in our half (closer to own goal), cover stays on the ball→own-goal line but maybe deeper: keep 2m goal-side (current) — or make it 2.5m? Don't over-tune. Keep current for defence.
Threshold: use midfield: if dist(ball, atk_goal) < dist(ball, own_goal) → attacking situation → support position. Else defensive line. Add hysteresis? The ball crossing midfield could flip the cover's target back and forth — but it's just a walk target, flapping costs little (robot walks a couple metres). Could add a small margin but keep it simple.
Also, should the support robot chase the ball if it's loose and very close? E.g., if ball within 1.2m of cover and presser is far... The presser assignment already switches on proximity with 1.5m hysteresis, so if the cover is genuinely nearest it becomes presser. Fine.
One more improvement candidate: kick targets always goal centre. When Zhi kicks from 1.2m at goal centre, the keeper-ish robot blocks. Aim at a corner? The shell could pick the far corner from the opponent nearest the goal... but we may not have opponent positions in obs — need to check what obs contains. The tail of team.py has _teammate; let me look. If obs has "opponents", I could aim at the corner away from the nearest opponent to the goal. That's a second change though. The playbook says one change per session where possible. The cover activation is the priority. But if turns/budget allow, kick-corner targeting is a cheap add... Let me first read the file tail, then decide. Keep discipline: one change, practice, lint, done. Maybe two small changes if both are surgical and practice verifies.
Actually, let me reconsider the biggest leak: 10 conceded. Even with perfect cover activation, if we concede 10 every week we lose. But their 62/64 touches mean they always had numbers around the ball; our activation fix addresses both ends (Pu will be around the ball more, both defending and attacking). I'll go with it.
Let me read the tail of team.py now. The read tool serves 24KB; the file is 9KB but got truncated in display because... hmm, actually the harness truncated it ("[truncated]"). Odd for a 9KB file, but fine — I'll read with an offset near the end, e.g., offset 6000, to get the tail.
Then edit, practice, lint, note, done.
Budget: $0.09 spent, $2.50 cap, 22 turns, 1h21m. Practice costs ~$0.03-0.05 each. Plenty. But tokens are the real cost — my reads re-send. Keep it tight: one read of the tail, one replace (or write), one practice, one lint, one note, done. That's ~6 turns.
Let me write the prose + JSON for reading the tail.
The digest tells the story plainly: 1–10 at home, and while the m23 fixes held (5 unforced falls, down from 15; deadlines near zero), Pu touched the ball **15 times** all match while Dynamo's pair managed 62 and 64 with the *same* player model. We are playing 1v2 around the ball — the cover robot is a spectator, and it's been top of my known-gaps list for two sessions. The read of team.py was truncated before the cover branch and helpers, so let me get the tail before I cut.
Readclub/team.py
The league answered3,737 characters
[read club/team.py@6000] rget": target}
if say:
out["say"] = str(say)[:120]
return out
# -- internals ------------------------------------------------------
def _ball(self, obs):
ball = (obs.get("detections") or {}).get("ball")
if isinstance(ball, dict):
xy = ball.get("field_xy")
if xy and ball.get("age_s", 0.0) <= BALL_MEMORY_S:
self.last_ball = [float(xy[0]), float(xy[1])]
return self.last_ball
def _teammate(self, obs):
for t in (obs.get("detections") or {}).get("teammates") or []:
if isinstance(t, dict) and t.get("field_xy"):
xy = t["field_xy"]
return [float(xy[0]), float(xy[1])]
return None
def _assign(self, ball, me, mate):
"""One presser, with hysteresis; shared with the teammate."""
shirts = self.shared.get("shirts") or {self.shirt}
other = None
for s in shirts:
if s != self.shirt:
other = s
prev = self.shared.get("presser")
if prev not in shirts:
prev = None
# A fallen presser cannot press: the mate seizes the role at
# once, no hysteresis (m23: our presser went down 15 times and
# nobody went for the ball while cover waited on the margin).
if prev is not None and prev != self.shirt \
and self.shared.get("fallen") == prev:
self.shared["presser"] = self.shirt
return self.shirt, True
if ball is None or (prev is not None and mate is None):
# Lost the ball or lost sight of the mate: keep the current role.
presser = prev if prev is not None else self.shirt
self.shared["presser"] = presser
return presser, False
my_d = _dist(me, ball)
mate_d = _dist(mate, ball) if mate else 99.0
if prev is None:
presser = self.shirt if my_d <= mate_d else other
elif prev == self.shirt:
presser = other if mate_d + SWITCH_MARGIN_M < my_d else self.shirt
else:
presser = self.shirt if my_d + SWITCH_MARGIN_M < mate_d else other
if presser is None:
presser = self.shirt
self.shared["presser"] = presser
return presser, (presser == self.shirt and prev != self.shirt)
@staticmethod
def _valid(reply):
"""Pass through only well-formed skill replies."""
skill = reply.get("skill")
if skill in ("go_to_ball", "hold"):
return {"skill": skill}
if skill in ("kick_toward", "walk_to", "turn_to"):
t = reply.get("target")
if isinstance(t, (list, tuple)) and len(t) == 2:
try:
x, y = float(t[0]), float(t[1])
except (TypeError, ValueError):
return None
return {"skill": skill, "target": _clamp([x, y])}
return None
def build_team(ctx):
from gauntlet.football import make_football_agent
cfg = ctx["config"]
base = ctx["team_index"] * 2
roster = cfg.get("players") or [{}, {}]
model = cfg.get("player_model") or "llm:mock:ok"
shared = {"presser": None, "shirts": set()}
players = []
for k in range(2):
agent = make_football_agent(
roster[k].get("model", model),
base + k,
seed=base + k,
prompt=roster[k].get("prompt", cfg.get("prompt", "football_v2")),
)
players.append(GLMPlayer(agent, base + k, shared))
shared["shirts"] = {p.shirt for p in players}
return {"players": players, "manager": None}
...[showing 6000-9654 of 9654 chars — end of file]
(0.0s)The league answered56 characters
model error (1/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 20s
The league answered56 characters
model error (2/3): APIConnectionError: Connection error.
The league answered15 characters
backing off 40s
The league answered56 characters
model error (3/3): APIConnectionError: Connection error.
Session over. Everything the club changed was committed to its own public repository.