邝埜 发表于 2017-3-10 00:30:49

一个有趣的发现,希望各位大佬给些看法

小弟最近在学习TNLB和Days系列规则,浅尝辄止
与之前已经有所了解的LaB30年规则和1x系列规则进行对比时,发现了一个有趣的现象
那就是所有部队的行军速度是不相等的

    单格长度(m) 单回合时间(h) 步兵基本速度(格) 双日行军路程(km/(18h×2))
LaB    100       0.33       7          75.6
Library  525       1.00       4          75.6
Days   1770       6.00       5          53.1
1x    3219       48.00       5          16.1

我们可以看到,定位为战术棋,完全不考虑高级战术兵团之间运动关系的TNLB和LaB系列,虽然尺度迥异,但最后的行军路程却殊途同归,说明它们模拟的步兵运动都是战场运动,只考虑单场战斗的胜败,步兵的推进速度极高
当尺度逐渐放大,到了days系列时,36小时行军速度已经下降了1/3
而到了1x系列的单回合2日时,双日行军速度仅仅只有16km
而表格中没有表现出的是,1x系列在这种行军速度下其实已经背负了很大的风险;如果战术兵团的人数超过30k人,掉队、开小差、伤病造成的非战斗减员会大幅度提升,这时所考虑的,已经完全是步兵的行军运动,而不是战场的快速突进了
此外我还做了另一张图,
data:image/png;base64,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
LaB在时空尺度上,几乎可以视为TLNB的简单放大,而后三者同为KZ的作品,似乎较有可比拟性。
单格地理长度与宏观行军速度呈现出接近简单线性的关系。(R2=0.9908)
请问各位dalao,这种设计,速度随着放大而变慢,是将那些因素纳入了考虑呢?
此外,别的棋是否有类似的规律呢?请各位dalao不吝解惑

illyj 发表于 2017-3-10 10:30:05

技术分析贴,赞一个!

主客二分 发表于 2017-3-10 14:52:32

本帖最后由 主客二分 于 2017-3-10 14:54 编辑

小弟这边网络有问题,看不到图片,非常支持邝埜大的技术分析
非dalao,尝试仅从军事的角度回答一下
按《战争论》和《战争与后勤》的说法,这种战役级行军缓慢一般是以下情况造成的:
1、庞大纵队通过狭窄道路时的拥挤和等待问题;
2、宿营花费过多时间;
3、补给车队跟进太慢,或者在道路上拥挤;
4、就地征集补给花费过多时间。
总结下来其实就是行军规划和军事勤务的组织问题以及后勤补给的问题。

berto 发表于 2017-3-10 22:46:48

这么比 肯定不行。。。撇开军事行动 接敌和普通行军速度神马的肯定不同。。。不能简单的进行比较啊 就拿时间尺度大了说 最简单的 吃喝拉撒什么的都得占用时间啊。。。不可能全都去行军。

类似的比较我也做过,我比较过同尺度下的TCS和panzer battle(mechwar2同系统的二战版系统,非MMP最新出的那个scs)。。。各个角度软硬毁伤能力 机动能力。。。千差万别的。。。

lurkc 发表于 2017-3-12 10:56:52

還沒有看懂,慢慢學習。支持你們

soul 发表于 2017-4-4 09:34:25

别的系统不说,单说LaB。LaB中的移动速度代表战斗/准战斗状态下的行军速度,不能放大到24小时。
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查看完整版本: 一个有趣的发现,希望各位大佬给些看法