篮球经理游戏出来了吗
先留言，后看
本帖最后由 zjh344353199 于 2021-12-30 22:18 编辑
很欣喜看到有国内团队（希望是）在做大规模的模拟经营游戏，这类游戏的系统设计和制作太困难了。
愚见，如果我没理解错，这项测试是为了验证球员”关键属性“是否真正关键，还有可能的基于阵容和指令的球员推荐系统。然而FM的球员报告仅是基于位置和职责的，同时FM的阵容和指令设定太多了，所以最后造成有不关键的“关键属性”是正常的。除非设计者将针对性指令直接关联属性或权值，而非影响模拟时球员的动作倾向；将阵容转换为与其他所有球员的平均距离，进而影响属性。同时在一些情况下，阵容和指令往往就是要服务于具体核心球员的属性，让“关键属性”变得更关键或更不关键。那如果改变了阵型设置和针对性指令会改变各位置的属性高亮，希望这个系统的最终效果不会让玩家觉得混乱。
所以我觉得如果是因为阵容和指令影响了“关键属性”的闭环不必过于担心，只要有足够多且合理的属性分类和指令，并且当你改变它时是有响应的。对于游戏开发更重要的是基于位置和职责给出的“关键属性”，（那或许是同一职责在不同阵型下的测试？）那毕竟是足球/篮球顾问的模型或模拟系统是否闭环的大问题。
另外，从游戏设计角度来说，不希望有向玩家开放的基于阵容和指令的球员推荐系统。那游戏里面真的还会有根据球员属性调整针对性指令的部分吗，还是输入一个当前版本的神阵然后等待球探推荐输出。那么可能需要一个另外的系统来制约，也可能消除了游戏性。
回头就是一炮 发表于 2021-12-30 10:05
见鬼，后腰双顺足那么重要，中后卫抢断并没有那么重要
中后卫大多时候主要就是顶头球和防身后，再加上长传找边路或者前锋，我觉得至少中后卫这一项跟我手打体感是一致的。双足后腰影响的是传球空间和精度，这代ai喜欢高位压迫。仅限个人理解
C·Gunners 发表于 2021-12-30 14:41
@恩凯迪亚 来看看这个帖子，理解下版本引擎变化和玩法，跟你想的完全不一样
你应该是针对我说射门用处不大这句换话来说的吧，我也没有好的模拟软件，就是直接套多特版本答案哈兰德模拟前十场比赛，状态全调满，手打五场跳过五场，用的就是zaz这个阵型，第一轮把哈兰德射门调低，第二轮把盘带调低，第三轮把决断调低，第四轮把速度调低，第五轮把爆发力调低，给你说说进球结果吧，进球总数从高到低排列
缺射门 缺决断 缺速度 缺盘带 缺爆发 可能我模拟没有那么准确，但是在缺少盘带的时候，明显球员失误增多，机会降低，缺乏速度和爆发的时候，进攻镜头明显减少，失误明显增多。缺射门决断的时候，射不进的次数有所降低。我这个测试肯定没有他的专业，但是我也相信能代表一些问题了，射门重要不重要？比前作重要了些，但是真的超过了双速吗，何况zaz这个阵型并不是多么要求前锋速度的阵型，在
我个人偏爱的442或者352都对顶头的前锋双速要求更高了，拖后的前锋技术盘带要求更高些，射门的高低的前锋我也实验课好几个，但是总体看下来，还是拥有双速的前锋变现更好一些，拖后或者桩峰在实际表现里，还没成型卢卡甚至不如技术更好的佩特科维奇。这都是我作为一个手打玩家的个人体验。
最后关于进攻效率这个问题，我相信zaz这个阵型已经属于进攻效率比较好的阵型了吧，我也是第一次用不清楚，说错了希望谅解，但是哪怕是多特蒙德在德甲，照样还是射门无数，哦进球可能多了不少，但是射门或者射正和进球比依然很大，更何况一般大家都选择的小球队了呢，这个版本进攻效率低下我相信很多人都认同吧，或者说射门率和进球比之间差距过大，如果是手打的体现就是各种射人射门将门框对吧，
最后，我还是看asayi1979这个大佬说的，目前进攻不止是看射门，而且要看预期进球那个，不同的射门方式在预期进球里的体现也不一样，这个说实话我之前没有太过于了解，但是我通过这个用了神阵我也发现了，除非是我用利物浦直类的碾压，或者面对强队或者是客场，对面门将超水平发挥的次数也是非常多的，但是整个赛季看下来门将的整体评分并不高。哪怕是我用了神阵，用了利物浦曼联，或者多特拜仁，在预期进球里的射门质量很差的情况也出现过多次，(我这是直接套的阵型，没有自己手调，为了保证一个稳定性)。神阵尚且如此，大家一般来说自己调的阵型，进攻效率只会更差一些，不是吗。
还是asayi1979提出的那个训练方法里的临时比赛加buff作用十分明显，用和不用差距巨大，大的甚至我觉得都可以算是小漏洞的方法了，这也证明fm还是需要一定量的改进不是吗。
最后，我还是早说，神阵只是一种选择，我相信不会影响大家对于阵法的研究，21下7.0神阵的人这个版本下5.4的，但是喜欢研究的玩家不管多少分，依然会自己研究不是吗，我个人就是对于442和352情有独钟，433也会大一些，上一代的五锋阵我根本没有用过，所以我说fm取消神阵弊大于利，因为他少了给玩家的一种选择，爱挑战的依然会挑战，不爱挑战的依然不会去挑战，但是我看创作者的热情并不会因为因为这个而改变，而玩家的游戏方法是没有高低之分的。因为大家爱的是足球，而非是数字，爱的是现实足球在游戏的的体验，爱的是自己拯救球队，执掌心爱球队的满足感，哪里会因为神阵有没有而影响自己的游玩方式呢！
楼主辛苦了，希望楼主继续加油，我还是很喜欢篮球的，希望早日看到游戏上市
这个数据分析我认为靠谱，基本和各位置关键属性贴合
Hi!
I'm the author of the tactic used in the experiments. I don't understand a single word of chinese and for that I apologize, but someone brought my attention to this post and I decided to register just to say how much I appreciate your work. I'm also a researcher of a similar field (optimization), so I really appreciate what you did there and it has high value to me. Thank you for your contribution!
If anyone can translate the table to english, I would gladly add it to the tactic main page and reference the authors of the study. Have a happy new year!
ZaZ 发表于 2021-12-31 10:03
Hi!
I'm the author of the tactic used in the experiments. I don't understand a single word of chi ...
卧槽
这是那个战术的作者？
这。。爆棚这么牛逼了吗，ZAZ的作者都来了？？
ZaZ 发表于 2021-12-31 10:03
Hi!
I'm the author of the tactic used in the experiments. I don't understand a single word of chi ...
你好！
我是实验中所用战术的作者。抱歉我不懂中文，但有人提醒我看下这个帖子，于是，我决定注册一下，只是为了表达我多么感谢你的工作。
我也是一个类似领域(优化)的研究员，所以，我很感谢你所做的，它对我有很高的价值。感谢您的贡献!
如果有人能把这贴子的研究翻译成英语，我很乐意把它添加到战术的主页上，并参考该研究成果。祝新年快乐!
sheap 发表于 2021-12-31 11:05
你好！
我是实验中所用战术的作者。抱歉我不懂中文，但有人提醒我看下这个帖子，于是，我决定注册一 ...
他说的是translate the table，应该是想要那个做好的表格的翻译版本？
ahstzl1989 发表于 2021-12-31 11:12
他说的是translate the table，应该是想要那个做好的表格的翻译版本？
应该是的 别人在fmarena上发给他的就是这个表的图片
ZaZ 发表于 2021-12-31 10:03
Hi!
I'm the author of the tactic used in the experiments. I don't understand a single word of chi ...
Basic description.
1, we are a basketball manager game development team, the development process reference study a lot of FM settings, including the game engine.
We have learned a lot about the game from the bursting shed, and the purpose of this post is to give back to the community
2, in order to analyze the mechanism of FM's game engine, as well as the degree of science, we designed a system for measuring the degree of influence of each player's attributes on the final victory or defeat in FM.
3. fm-arena.com gives a preliminary test of the relationship between player attributes and wins and losses in FM2021 and FM2022, which is of some reference value and inspiration for our work
However, as it is only the non-professional work of amateurs, from the point of view of rigour, there are the following problems
1) It only gives the results of which attributes have a greater and lesser impact on winning and losing for all players, but in reality, the key attributes are obviously different for different positions, and the results of the test clearly show that only those attributes that are important for all positions will be more important, while those attributes that are important only for certain positions will be less important in the test. For example, its test results show that shooting has almost no effect on winning, while explosive power has a big effect on winning and losing.
2) Its test sample is insufficient, its test for each attribute was only carried out for about 900 matches simulated, but for a normally randomly distributed sequence, in general it needs to be randomized at 10,000 times before it converges relatively well to the mean.
(3) The effect of attributes on wins and losses is non-linear, and the test only deducts 4 points from the attribute to investigate whether it has an effect on wins and losses, but sometimes, just because 4 points have no effect does not mean that 8 points also have no effect, and it is also possible that 2 points have an effect that is close to 4 points.
4) There is a correlation between the impact of attributes on wins and losses, and its test of only changing one attribute at a time to test the impact on wins and losses can be interfered with by the correlation. For example, a breakthrough can lie on the ball to change direction and accelerate past a player, relying on physicality but not on discography, or it can rely on discography. If a player is good at both physicality and discography, then reducing his discography will not significantly affect his breakthrough effectiveness, as he can accelerate past people with a lie-back change of direction instead of discography.
5) Different tactics require different attributes for each position of the player, and it is not rigorous to talk about which attributes have an impact on winning or losing away from specific tactics.
4, we use artificial intelligence to conduct attribute importance research, artificial intelligence technology used a deep neural network similar to Go Alpahgo.
We fixed the tactic as the strongest tactic on fm-arena.com, ZaZ-Blue DM, for this experiment in order to analyse which attributes of players in each position have a greater impact on wins and losses under this tactic.
We built an artificial intelligence system and trained the AI to design the best attribute assignment for each position's performance with the same CA for each player in the whole team.
The approximate process was as follows: first, each attribute at each position was assigned the same value, then the neural network would try to change the value of certain attributes at certain positions (keeping CA constant) to see if the change had a positive or negative impact on wins and losses, and thus iterate over the multi-layer network to know what the AI thought was the best combination of 11 players.
We obtained the convergence results by training roughly 140 machines for 3 weeks and simulating 40 million games.
5. To verify whether the best 11-player attribute allocation given by the AI is really the best, we conducted another result validation test
Three groups of teams were designed, one with evenly distributed player attributes, another with the best 11-player attribute assignment given manually by experienced players based on their gaming experience, and the last group given by the AI. Put into a test league for 100,000 games, the AI's solution was significantly better than the human players' solution and far superior to the even distribution.
Conclusion.
For the ZaZ-Blue DM tactic, for each position we obtained the following levels of importance for the attributes (the values were normalized by 5 for ease of viewing, which should be sufficient accuracy for the game).
Significance of use.
1, players can get the key attributes of each position according to the above table, 100 is the most critical attribute, while 1 is the least critical attribute, so as to guide the selection of materials.
2. The values in the above table can also be used as attribute weights to calculate the weighted average value of the attributes and then calculate the "Tactical True CA"
Tactical True CA = Weighted average value of attributes * 20 - 121
If the Tactical True CA is higher than the Player CA it means that the player is a good fit for the ZaZ-Blue DM tactics, the higher the Tactical True CA the better the fit.
This is used for player selection
For the convenience of future players playing in this way, the table we have given above is arranged according to the order in which the attributes are displayed in the player's interface, even if you don't write the program, you can also get the "Tactical True CA" in excel after quickly and manually entering the values of the attributes in three rows
3. For guidance on training, the attributes take up CA, so in order to train the players with the highest tactical true CA, we can train the attributes with the highest cost effectiveness
The cost effectiveness of each attribute for each position can be measured by the attribute Tactical True CA Weight / Attribute CA Weight, the higher the value, the higher the attribute will make the player's attribute Tactical True CA higher if it increases the same CA.
Main limitations.
The current experiments are costly and non-replicable, engine versions are changed, tactics are changed, and the AI needs to be retrained without migration learning, so our next phase will focus on migration learning, where the results obtained from iterative training in the case of a certain version of a certain tactic are used as the basis for a new engine and a new tactic, and rapid iterations are made to obtain results in a new environment.
本帖最后由 ykykyk05251 于 2021-12-31 12:23 编辑
ahstzl1989 have translated the table，thx
ykykyk05251 发表于 2021-12-31 12:05
Hi,
The order of the attributes in the table is the same as the player interface in the game{ ...
你用韩国那个皮肤了吧 属性排序和原皮排序不一样的
ahstzl1989 发表于 2021-12-31 11:12
他说的是translate the table，应该是想要那个做好的表格的翻译版本？
对啊，贴子的研究
原来Zaz大佬是优化领域的研究员....四舍五入我也是同行为什么我就做不出神阵555
训练AI在全队每个球员CA不变的情况下，设计出每个位置战绩最佳的属性分配。
训练AI在全队每个球员CA不变的情况下，设计出每个位置战绩最佳的属性分配。楼主可否把这一分配对应的属性和相关八卦图发来看看？虽然八卦图损失了很多具体细节，但是我觉得对球员的第一印象还是很关键的，有时候看一眼就大概知道这个球员适不适合我的战术。感觉这样的图可以对已有的研究有更好的说明？最后再赞一次，很棒很棒的研究，给了我新开一档的动力哈哈哈