An economic model for a rickshaw driver’s decision-making.

1. The driver’s real objective

The naive model is:

“Take any fare above cost.”

That is wrong.

The better model is:

The driver maximizes expected daily net income, subject to fatigue, risk, time loss, and a cash target.

So the driver is not evaluating a ride in isolation. He is evaluating:

• money from this ride
• time consumed by this ride
• probability of getting the next ride after this one
• risk of dead mileage after drop-off
• mental/physical hassle
• how close he already is to his daily target

That means every passenger request is really a portfolio decision, not just a yes/no to immediate revenue.

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2. The core unit: value of one ride request

For any offered trip , define:

Immediate economics

• = fare earned from the trip
• = direct operating cost of the trip
  (fuel, wear and tear, tiny incremental costs)
• = time spent on the trip
  (pickup + driving + traffic + unloading/waiting)

Post-trip position

• = expected repositioning cost after drop-off
  (empty return distance, wasted fuel, time to next fare)
• = expected quality of destination for the next fare
  (station good, dead-end colony bad)

Friction / hassle

• = non-monetary hassle cost
  (difficult passenger, luggage, rain, bad roads, police zone, dispute risk)

Then the expected net value of accepting ride is:

V_i = F_i - C_i - R_i - H_i + Q_i

But that still misses the time value.

So divide by total expected time commitment:

\Pi_i = \frac{F_i - C_i - R_i - H_i + Q_i}{T_i}

This is the driver’s effective expected earnings per minute from taking that ride.

That is the real decision variable.

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3. Acceptance rule

The driver accepts a ride if:

\Pi_i \geq \theta

Where is the driver’s reservation earnings rate: the minimum expected earnings per minute needed to make the ride worth it.

This threshold is not fixed. It changes during the day.

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4. What determines the threshold

A. Daily income target

Let:

• = target income for the day
• = income already earned by time

If the driver is far below target, he may lower his threshold to keep cash flowing.

If he is close to target, he becomes choosier.

So:

\theta_t = \theta_0 + a(\text{fatigue}) + b(\text{time scarcity}) + c(\text{distance from target, with sign})

A more intuitive form:

• early in the day: moderate selectivity
• midday slump: take more rides if demand is weak
• near target: reject annoying or low-yield rides
• very bad day: threshold may fall sharply because liquidity matters

This helps explain why the same driver may accept a ride at 11 a.m. and reject it at 8 p.m.

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B. Fatigue

As the day goes on:

• willingness to tolerate hassle falls
• willingness to drive to awkward areas falls
• the subjective cost of traffic rises

So fatigue raises and often raises .

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C. Demand conditions

If demand is strong, the opportunity cost of a bad ride is high.

If demand is weak, waiting is dangerous, so even mediocre rides become acceptable.

So the driver compares:

\Pi_i \text{ vs expected value of waiting}

Call the value of waiting .

Then the real rule is:

\text{Accept ride if } \Pi_i \geq W_t

This is even better than the fixed-threshold version.

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5. The value of waiting

Drivers are constantly making this comparison:

Option 1: take current ride

earn something now, but get tied up

Option 2: wait

hope for a better passenger very soon

Let:

• = probability of getting another fare soon
• = expected value of the next fare
• = waiting time cost

Then:

W_t = p \cdot E[\Pi_{next}] - w

If the current passenger offers less than this, the driver rejects.

This explains several common behaviors.

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6. Why short rides get refused

A short ride often has:

• low fare
• almost fixed boarding/search time
• possible poor drop location
• high chance of empty repositioning

So even if the passenger thinks:

“It’s nearby, easy money.”

The driver may calculate:

“This blocks me for 12 minutes, pays too little, and leaves me in a bad spot.”

In model terms:

\Pi_{short} < W_t

So refusal is rational.

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7. Why destination matters so much

Destination affects three things:

• future passenger density
• empty return probability
• traffic escape difficulty

So a ride to a railway station, market, hospital, or main road may have high .

A ride deep into an interior area may have low or negative .

That means two identical fares are not equal.

Same fare, different future value:

• Trip A to station:
• Trip B to dead-end colony:

So drivers are not just pricing the ride.
They are pricing the next 30 minutes of their day.

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8. Why bargaining happens

Suppose the official metered fare gives:

\Pi_i < W_t

Then the driver has two choices:

• reject
• quote a higher fare so that the trip crosses the acceptance threshold

Let required fare be . Then:

F_i^* = \theta T_i + C_i + R_i + H_i - Q_i

This is the minimum acceptable fare.

If the official fare is below that, bargaining appears.

So bargaining is not random greed. It is often an attempt to push the ride up to the driver’s internal reservation price.

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9. Why drivers cluster at certain locations

Drivers prefer locations where:

• , probability of next fare, is high
• expected fares are decent
• trip distribution is favorable
• idle time is socially tolerable
• police/enforcement risk is manageable

So stands near stations, malls, hospitals, schools, and busy junctions become rational waiting nodes.

These are not just physical locations. They are high-option-value assets in the driver’s daily strategy.

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10. Ownership model changes behavior

There are at least three broad driver types.

A. Owner-driver

Pays capital cost/EMI, but keeps full upside.

Behavior:

• more sensitive to maintenance
• more strategic long-term
• may tolerate lower fares if vehicle utilization matters

B. Rental driver

Pays daily fixed rental to owner.

Behavior:

• strong pressure to first “clear the rental”
• early-day desperation can be high
• after clearing rental, behavior may change sharply

C. Fleet/app-attached driver

Faces platform commissions, digital matching, less bargaining freedom.

Behavior:

• lower search cost
• weaker control over passenger selection
• more algorithm-driven utilization

So the same road behavior can only be understood after knowing the contract structure.

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11. Add a simple daily target model

Let daily target be:

Y^* = D + M + S

Where:

• = household cash need for the day
• = vehicle/operating obligations
• = precautionary or aspirational surplus

The driver stops or becomes selective once expected additional work has low marginal utility.

Utility can be written as:

U(Y, E) = u(Y) - v(E)

Where:

• = income earned
• = effort/fatigue

And has diminishing marginal utility after subsistence and daily obligations are met.

So after target income is reached:

• one more awkward trip is much less attractive
• refusal rates rise
• preference for close/easy trips may rise

This is one reason evening behavior may feel inconsistent to passengers.

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12. Behavioral overlays

The pure economic model is good, but incomplete. Real drivers are not calculators. They use shortcuts.

Heuristic 1: “Bad destination”

Certain neighborhoods acquire a reputation. Drivers may reject quickly without precise calculation.

Heuristic 2: “Short ride not worth it”

A rough rule evolved from repeated low-value outcomes.

Heuristic 3: “Rain surcharge mentality”

Even where illegal, drivers know rain raises both demand and hassle.

Heuristic 4: “Last ride home”

Near end of shift, drivers want rides that align with their own route.

So actual behavior is:

\text{Decision} = \text{economic threshold} + \text{habit} + \text{local norms} + \text{mood}

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13. A compact decision tree

When a passenger approaches, the driver implicitly asks:

Step 1: Where are you going?

This estimates , , and .

Step 2: How much will this pay?

This gives .

Step 3: How annoying will this be?

This estimates .

Step 4: What happens if I wait instead?

This gives .

Step 5: Where am I relative to my daily target?

This changes threshold .

Then:

• if current ride beats waiting, accept
• if not, reject
• if close, bargain

That is the whole machine.

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14. A very usable formula

For workshop or analytical purposes, use this:

A_i = F_i - (C_i + R_i + H_i) + Q_i - \theta T_i

Where:

• if , accept
• if , bargain
• if , reject

This is a very clean operational model.

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15. What this model explains well

It explains:

• refusal of short rides
• destination discrimination
• preference for stands and transit nodes
• bargaining above meter
• time-of-day variation
• why some drivers are highly selective
• why drivers behave differently after hitting target income
• why passengers misread “easy trip” economics

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16. What the model does not fully explain

It does not fully capture:

• union or stand politics
• police harassment or bribe risk
• city-specific permit distortions
• social identity and class conflict
• app-platform algorithmic manipulation
• extreme liquidity stress at household level

These can be added as external parameters.

For example:

• enforcement risk =
• political/stand control =
• app commission =

Then:

A_i = F_i - (C_i + R_i + H_i + P_i + K_i) + Q_i - \theta T_i

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17. The deepest intuition

A rickshaw driver is not selling rides.
He is selling time slots of vehicle capacity under uncertainty.

Each passenger request competes with:

• the possibility of a better next passenger
• the possibility of a worse destination
• fatigue
• daily cash needs
• search time
• urban geography

So the right mental model is not:

“Why won’t he take my fare?”

It is:

“Why does this ride lose against his next-best alternative?”

Once you frame it that way, most driver behavior becomes intelligible.

18. One-line version

A rickshaw driver accepts a trip when its expected net earnings per minute, adjusted for destination quality and hassle, exceed the expected value of waiting for the next fare.

Why does the Indian auto-rickshaw exist?

The Indian auto-rickshaw exists because it solves a very specific transport problem: millions of short, irregular, low-budget trips in dense, chaotic, fast-growing cities where buses are too coarse, taxis are too expensive, roads are too narrow, and walking is too slow. In transport terms, it is an intermediate public transport mode: more flexible than a bus, cheaper and smaller than a taxi, and better suited to congested streets than a car. WRI India describes auto-rickshaws as crucial for first- and last-mile connectivity, with a fleet of over 7 million vehicles, while city-level research repeatedly treats them as a core part of the urban mobility system rather than a marginal add-on. 

The origin story

The auto-rickshaw is the motorized descendant of older pulled and cycle rickshaws. India did not invent the broader “rickshaw” idea, but it localized and industrialized the motorized three-wheeler into something unusually durable and scalable. Bajaj Auto says it received its Indian manufacturing license for two- and three-wheelers in 1959, launched its first three-wheeler goods carrier in 1971, and launched the rear-engine Bajaj RE autorickshaw in 1977; the company itself describes that vehicle as something that soon became ubiquitous on Indian roads and a recognisable symbol of India. 

That timeline matters. Post-independence India was capital-scarce, road-constrained, and only gradually motorizing. So the “average Indian rickshaw” did not emerge because planners designed an ideal vehicle from scratch. It emerged because the country needed something cheap, repairable, low-fuel, narrow-bodied, and commercially viable at small scale. A three-wheeler was the sweet spot: materially lighter and cheaper than a car, but motorized enough to outperform cycle rickshaws and carry passengers or goods all day. Bajaj’s long dominance helped standardize this format, which is why the silhouette became so uniform across cities. 

Why it looks the way it does

The Indian auto-rickshaw is basically a machine optimized for urban scarcity. Its small footprint lets it thread through congestion and narrow lanes; its high seating position and open sides make rapid passenger entry and exit easier; its low engine displacement keeps operating costs down; and its simple mechanical layout makes repair possible in decentralized workshops rather than only in formal dealerships. Bajaj’s current RE specifications still reflect that logic: small displacement engines, modest power, and a package built around utility rather than comfort or speed. 

So why not a tiny car instead? Because the rickshaw is built around a different economic equation. It sacrifices weather protection, crash protection, luggage space, and comfort in exchange for lower acquisition cost, lower running cost, and better maneuverability. That is why it thrives in the gap between the bus and the car. In a city with uneven demand, a vehicle that can be profitable on short hops and survive bad roads matters more than one that offers a “better” ride in engineering terms. WRI notes that auto-rickshaws remain relatively inexpensive to purchase or rent compared with larger vehicles and are viable employment generators. 

Why India adopted rickshaws instead of only buses or taxis

Because buses and taxis solve different problems. Buses are efficient for corridors with predictable demand. Taxis are efficient for people who value comfort, privacy, or time enough to pay more. But Indian cities have vast amounts of fragmented demand: station-to-home, market-to-colony, office-to-main-road, school-to-lane, short trips at odd hours, and trips in places where the road network is physically hostile to big vehicles. That is rickshaw territory. Research and policy work around metro access repeatedly treats autos and e-autos as important connectors to mass transit, especially for last-mile access. 

This is why the rickshaw keeps surviving even after metro growth and app taxis. It is not just “cheap transport.” It is a gap-filler in a system full of gaps.

Why it evolved the way it did

It evolved under four big pressures.

First: city growth and congestion. As Indian cities sprawled outward, demand rose for flexible point-to-point travel in suburbs and peri-urban areas. The growth in registrations reflects that. WRI cites an annual 8.2 percent growth in autorickshaw registrations over the last decade based on MoRTH data analysis. 

Second: regulation. Auto-rickshaws are not a free market in the pure sense. Permits, fare rules, route rules, fitness requirements, and fuel mandates shape the sector. In Maharashtra, the Khatua Committee was explicitly set up to review fare fixation because the economics had changed and the older framework had become contentious. The same report also notes enforcement shortages and complaints around fare refusal. 

Third: fuel and emissions policy. Many cities pushed autos away from dirtier fuels toward CNG, and now toward electric. Delhi’s current draft EV policy provides year-wise incentives for e-auto adoption and explicitly covers replacement of old CNG autos or registration of new e-autos with Delhi permits. At the national level, NITI Aayog says India has been doing relatively well in electric two- and three-wheelers compared with other EV segments. 

Fourth: informal labor supply. The sector kept growing because it could absorb workers who had limited formal credentials but needed urban cash income. That matters as much as transport demand itself. 

Why drivers behave the way they do

This is the part most passengers notice first, and usually misread.

A lot of “bad behavior” is not random rudeness. It is the visible output of a system with thin margins, waiting time risk, weak enforcement, controlled fares, and asymmetric information.

1. Why do drivers refuse short rides?

Because the system often makes short rides unattractive relative to time spent. Maharashtra’s fare committee explicitly argued that too small a minimum-fare distance would reduce driver incentive and could increase fare refusals. That is a striking admission: refusal is not just a moral failure but partly a pricing-structure problem. The same report preserved a 1.5 km minimum stage partly for this reason. 

More broadly, WRI notes that the current fare system often fails to account for the opportunity cost of labor during idle time and off-peak waiting. If a short trip dumps the driver in a low-demand area, the real economics of that ride may be poor even if the meter technically runs correctly. 

2. Why do drivers bargain or avoid meters?

Because fares are regulated and often slow to adjust, while fuel, maintenance, and household costs move faster. WRI says regulated fares have been slow to get revised and links this to reduced earnings and financial stress. Maharashtra’s committee was created precisely because fare-setting had become contested under changed economic conditions. 

So a driver bargaining is often trying to restore what he sees as the “real” fare for conditions the official tariff does not price well: traffic, destination risk, empty return risk, luggage hassle, rain, or very short distance.

3. Why are drivers sometimes selective about destination?

Because destinations are not equal. A destination near a station, market, hospital, school cluster, or arterial road has a high probability of the next fare. A destination inside a dead-end colony, traffic choke, or low-demand zone can waste time. Since the driver’s day is a sequence of uncertain matching decisions, he is constantly optimizing not just the current ride but the next one too. That is rational behavior in a market with no guaranteed utilization. This is an inference from the fare-stage logic, idle-time economics, and demand thinning during lean hours noted by official and WRI sources. 

4. Why do some drivers seem aggressive or distrustful?

Because repeated low-trust transactions create defensive norms on both sides. Drivers expect some passengers to underpay, haggle, cancel, or complain opportunistically. Passengers expect overcharging, refusal, and route games. Once both sides assume the other may exploit them, courtesy falls and transaction speed rises. Official reports mention fare-refusal complaints and enforcement problems; academic work on Indian autos also frames the sector as caught between public criticism and real economic hardship. 

So the “attitude problem” is partly a trust problem, not just a character problem.

Who becomes a rickshaw driver?

Usually someone entering or surviving in the urban informal economy.

The evidence is not perfectly uniform across India, because different studies are local. But the pattern is clear: auto-rickshaw driving attracts lower-income men, often migrants or children of migrants, with limited formal education, modest assets, and a need for work that offers immediate cash flow without requiring a degree or office gatekeeping. Recent socioeconomic studies describe drivers as a marginalized informal workforce; WRI describes the vehicle as a viable employment generator; and WRI’s e-rickshaw work says e-rickshaw operators are often unskilled laborers shifting from factory work, cycle-rickshaw pulling, or daily-wage labor. 

This does not mean every driver is desperate or unsuccessful. Some own their vehicles, know their market well, and do decently. But structurally, the sector is an absorber of labor that the formal economy either cannot or will not absorb quickly enough.

A useful way to think about the typical entry path is:

rural or small-town origin → migration or semi-urban settlement → low educational capital / weak formal job pipeline → need for daily cash income → entry through rental, owner-driver purchase, or community network.

Financing models aimed at drivers reinforce this picture. NITI/ADB’s EV financing report highlights specialized models like Three Wheels United that lend to auto-rickshaw drivers using nontraditional credit assessment, including savings behavior and household conditions, precisely because standard formal credit tools often do not fit this population well. 

Is it a temporary job or a profession?

Both.

For some, it is a fallback or transition occupation. For others, it becomes a long-term livelihood because the barriers to moving upward into formal work remain high. This is why the sector often behaves like a hybrid: economically informal, but socially recognized; precarious, but persistent; low-status, but indispensable.

The Maharashtra fare committee’s attention to cost of living, family welfare, health, education, pension-like concerns, and even a proposed welfare and insurance corporation shows that the state itself recognizes drivers not as casual passers-by, but as a durable occupational group with family responsibilities and welfare needs. 

The economic model of the average auto

The economics are simpler than they look, but harsher than many passengers assume.

A driver’s revenue is the total day’s fares. Against that come fuel or charging, maintenance, permit-related costs, insurance, license renewals, downtime, and sometimes rental or EMI burden. WRI notes that many autos are financed through loans, from both formal and informal lenders, and says falling incomes have contributed to some of the highest default rates in the automobile sector. The same source says many drivers report driving around 100–120 km per day and earning roughly ₹7,000–₹9,000 per month on average in the study it cites, with take-home barely enough after operating costs. 

That helps explain why the driver often behaves like someone optimizing cash flow, not customer delight. If margins are thin, the driver becomes highly sensitive to destination quality, idle time, and enforcement probability.

Why permit systems matter so much

Permits are one of the biggest reasons the sector looks irrational from the outside.

Where permits are capped or difficult to obtain, the right to operate becomes scarce and valuable. That scarcity can support earnings for incumbents but also create distortions: rent-seeking, informal transfer markets, concentration of control, or a gap between the legal permit holder and the actual driver. Maharashtra’s committee explicitly recommended linking permits to Aadhaar and requiring the permit holder’s card and photo on the permit to stop misuse or illegal transfer. That tells you the problem is real enough to need formal correction. 

So the auto-rickshaw sector is not just a transport market. It is also a market in operating rights.

Why the system is messy city to city

Because “Indian rickshaw” is not one single institution.

Mumbai, Delhi, Kolkata, Bengaluru, small-town Uttar Pradesh, and rural Bengal all use similar-looking vehicles under quite different operating logics. Some cities use meter-based solo rides. Some rely heavily on shared autos. Some have route-based informal structures. Some shifted to CNG earlier. Some are moving faster to e-autos. Even within Maharashtra, the committee noted that meter feasibility and fare structure issues differ across urban, rural, hilly, and low-demand regions. 

So when someone says “rickshaw drivers are like this,” the safer answer is: which city, which submarket, and which time of day?

Why passengers hate the sector and still depend on it

Because it combines indispensability with friction.

Passengers hate uncertainty: refusal, bargaining, route disputes, or bad behavior. But they keep using autos because the mode solves trips that other modes solve badly. That tension is built into the sector. Academic summaries note that auto-rickshaws play an important role in Indian cities even while drivers face considerable criticism and the sector remains politically contentious around permits and fare-setting. 

In other words: the sector is not beloved because it is elegant. It survives because it is useful.

Why the average Indian rickshaw still exists in the app era

Because apps changed dispatch, not the underlying need.

Ride-hailing can reduce search time and fare disputes, but it does not eliminate the economic logic of short-distance, low-cost, small-vehicle travel. Even app-linked auto markets still depend on the same urban geometry: narrow roads, short trips, fragmented demand, and price-sensitive riders. New mobility programs around metro access continue to treat autos and e-autos as practical feeder services, not legacy relics. 

The electric shift

The future of the sector is probably more electric, not less rickshaw-shaped.

Delhi’s draft EV policy now offers explicit incentives for e-autos, including replacement of old CNG autos. NITI Aayog says India has relatively stronger EV progress in two- and three-wheelers than in cars. WRI says Vahan recorded more than 18.1 lakh e-rickshaws by November 2024, and that e-rickshaws often do not require a separate permit, which has supported rapid uptake. 

That said, the electric transition is uneven. WRI also notes that battery choice, upfront cost, and financing remain major bottlenecks, especially for lower-income operators. So the likely outcome is not a clean, top-down replacement. It is a messy coexistence of CNG autos, legacy ICE autos, e-rickshaws, and city-specific policy experiments. 

A simple way to understand the whole system

Think of the average Indian auto-rickshaw as the product of five constraints:

1. Urban form: dense, irregular, congested cities.

2. Income reality: many riders need cheap mobility.

3. Labor reality: many drivers need low-barrier income.

4. Regulation: permits and fares are politically managed.

5. Trust deficit: both sides expect opportunism.

Once you see those five together, most of the strange features make sense.

The vehicle is small because roads and wallets are small.

The sector is huge because the mobility gap is huge.

Driver behavior looks abrasive because margins and trust are thin.

The labor pool skews vulnerable because entry barriers are relatively low.

The system persists because India still needs a flexible middle layer between walking, bus, and taxi.

So the deepest answer to “why does the Indian rickshaw exist the way it does?” is this:

It is not an accident or a cultural quirk. It is an adaptive response to Indian urbanism. It evolved to serve cities that are crowded, unequal, under-integrated, and constantly in motion. And the drivers behave the way they do not because they are uniquely irrational, but because the system rewards some behaviors, punishes others, and leaves very little margin for error. 

Arsenal v/s Tottenham

Arsenal 5 - 2 Tottenham!
What a day to be a gooner, ;)

The gunners hosted their north London rivals in the EPL this weekend after 2 consecutive losses. The spread was suspended by the 70th minute when Arsenal came from 2 goals behind to score 5 consecutive goals.

The graph shows how incredibly volatile the prices were.

Orissa in the Ranji Trophy

This team came in last place in the recently concluded Group A of the Ranji Trophy Elite league. Their 2011 season did not include a single win in 7 games; and all they had to show for their efforts were 3 draws among 4 losses.

The team's position at the bottom of the table is a clear function of a squad that mostly consists of bit players with poor stats. None of them are household names and one can't see this situation changing any time soon. Just 3 of their batsmen have a batting average above 30.0; while only 1 bowler has a bowling average below that.

On a positive note, any youngsters about to break into the state team can eagerly await a call-up as this is one side that's carrying a lot of deadwood. There should be quite a few spots available in this Orissa side by the start of the next season. Also, Orissa has a history of providing its youngsters with opportunities to play first-class cricket, and this squad had an average age at debut of just over 21.

The 16 member squad has an average age of 25, the youngest member is 20 while the oldest among them is 30. Their entire cumulative experience amounts to 251 matches over 61 seasons. The relative inexperience of the team is mostly on account of the inclusion of 5 debutants in the squad this season.

Players who should be let go:
The following 5 players have had the opportunities to prove themselves at this level, but haven't been able to make a case for continued selection in the future. Some of them have surprisingly poor stats and this may well be a reflection of the lack of competition for spots in this team. However, its about time some youngsters were blooded here, at any rate they'd have some difficulty being worse than these five.

1. The oldest member of the team is 30 year-old Subit Biswal, who should be a very worried man. He made his debut in 2002 at the age of 20, but has played only 27 matches over the last 10 seasons. Having bowled only 18 deliveries at the first class level his primary role is very obviously that of a batsman. However, with a career batting average of only 22.93 he ranks only 132nd among all players in this league. I'd be very surprised to see him return for next season.
2. Another candidate for relegation is 27 year-old Paresh Patel. Another lad who made his debut at the age of 20 in 2005, but has played only 17 matches over the last 7 seasons. Paresh has a batting average of just 24.59 from 27 innings and 8 wickets at almost 71 runs apiece. He comes in at 127th in the batting average ranks.
3. 27 year-old Bikas Pati is a batsman with an incredible average of just 19.35 from 48 innings. His first-class career has spanned 30 matches over 6 seasons. How he managed to keep his spot so long is definitely a point to ponder. He ranks 150th in the batting averages, and with just 20 legal deliveries to his name he doesn't make a case for inclusion as a part-time bowler either.
4. Dhiraj Singh, a 25 year-old spin bowler made his debut in 2008. Since then he has a tally of 45 wickets from 17 matches at a very expensive average of 41.56 apiece, which puts him outside the top 100 bowlers in the league.
5. Preetamjit Das is a 26 year-old medium pacer who doesn't get too many matches. He made his debut in 2006 but has played only 11 games since then. With a haul of 25 wickets at an expensive 34.60 runs apiece he ranks 74th in the league. He gets his wickets at an average of 2.27 every match, has an economy rate over 3.1 per over and strikes every 66 deliveries. He just might be able to hang onto his place if there aren't any decent young bowlers pushing for a place in the state team.

Lone-star:
A rare bright spot in this dreary mire

1. 26 year-old medium pacer Basant Mohanty is a rarity in this team, he's ranked 10th in the overall bowling averages. Having made his debut at 21, he's now played 34 matches over 5 seasons. With 125 wickets at 23.54 runs each, he averages 3.68 wickets per match, leaks only 2.44 runs every over and gets a wicket every 57.8 deliveries. At this level, Basant ranks at 13th for wickets per match and at 14th for his economy.

Watch-list candidates:
These players should be on notice to raise their performance levels, but considering that there is so much dross to get rid off before the next season they might well be safe.

1. 24 year-old Natraj Behera is easily the best batsman in the team. With 15 matches from 4 seasons, he has an average of 40.52 from 25 innings, which ranks him in the top 50 batsmen in the league.
2. Another 24 year-old Biplab Samantray made his debut in 2010. He has played 16 matches over 2 seasons, with an average of 33.52 from 26 innings, which places him at 85th spot.
3. Subhrajit Sahoo is a 24 year-old batsman with an average of 29.26 from 24 innings over the last 3 seasons, which places him outside the top 100 batsmen in the league.
4. Halhadar Das, the 26 year-old wicket keeper averages 2.97 catches every match but manages a stumping only once every 10 games, which may well be an indictment of the quality of spinners in the squad. He is handy with a bat and has a batting average of 32.59 from 52 innings. 

Olympiakos v/s Arsenal - Champions League

Arsenal to win / draw @ 2.75

A tempting price on offer due to a younger and relatively inexperienced version of a gunner team playing away in Greece. With the home team facing a potential spot in the pre-quarters, they will be very up for the game as will their support. Which Arsenal team turns up will pretty much decide the outcome.

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Half-time update: Olympiakos 2 - 0 Arsenal

The graph below shows how the spreads moved during the first half an hour of the game. The second goal widened the spreads so far that I haven't bothered depicting it here.

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Full-time update: Olympiakos 3 - 1 Arsenal

Unfortunately, the crappy second string turned up for the gunners. The price on Arsenal to win became laughable after the first 30 minutes.

Carling Cup Quarterfinals - Arsenal v/s Manchester City

I really should have taken a position on City to win / draw; but didn't considering Arsenal were playing at home. The only player on the field who noticed the home fans was probably Nasri, as he got booed everytime he got anywhere near the ball. The usually sedate Arsenal home crowd was in good voice throughout the game just for heckling him. Anyways, the movement of the spreads doesn't show it, but the match flowed both ways, with both teams having opportunities to get ahead. Only a late defensive lapse by Arsenal led to their exit here.

Prices on Arsenal to win reached ridiculous levels (upwards of 30.00) after City scored the only goal in the game.

Arsenal v/s Borussia Dortmund

Arsenal to win / draw @ 1.5

The gunners really should seal their spot in the knockout stages of the UEFA Champions League with a win at home.

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Half-time update: 0-0

The price doesn't really reflect the Germans efforts so far. The visitors have done well to pierce the Arsenal defense on quite a few occasions. Their supporters have also been in better voice than the usual insipid home crowd at the Emirates.

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Full-time update: Arsenal 2 - 1 Borussia Dortmund

The second half was a completely different game. Arsenal came into their own, scored 2 goals and (being Arsenal) took their foot off the gas to let the German side get a consolation goal, during injury time. I stopped tracking all prices by the 80th minute, and in fact the price on Dortmund to win became ridiculous by the 65th minute itself.

England v/s Sweden - friendly at Wembley 2

England to win @ 1.80

After their very Italian style victory against the World champions last weekend, this English side should be odds on favourites for a victory against the visiting Swedes this evening. In fact, i'll go out on a limb here and state that the prices offered for a home victory are worth taking on and i'd expect these to reduce over the course of the match.

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Half-time update: England 1 - 0 Sweden

The above graph of the prices tells the story of a one way match. The spreads took off after the 25th minute, thanks to an own goal by the Swedish. In fact, the spread should have been even wider with England at least 3 goals up. However, quite a few botched chances have kept the Swedes in this game and I wouldn't rule out an equaliser.

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Full-time update: England 1 - 0 Sweden

The spreads continued to widen in the second half, though nothing material really happened. The English couldn't get another goal, and the Swedes had a few shots at the target too. I stopped tracking the price on a Swedish victory as early as the 65th minute; the runaway prices (at one point in the game it was over 100:1) were playing havoc with the scales on the graph.

England v/s Spain - friendly at Wembley

Spain to win / draw @ 1.30

A weakened English side should pose no difficulty to the all conquering Spanish (Barcelona) side. Got to feel for Capello, chap's missing the wedding of his son for this.

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Half-time update: 0-0 at Wembley

Surprisingly flat at Wembley in the first half. The Spanish have dominated possession but have nothing to show for their efforts as yet. They're looking more like Arsenal rather than Barcelona. I could fancy England to steal a goal here, against the run of play.

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Full-time: Spain lose!

As surmised, England scored early in the second half and never looked in trouble after that. A little luck along the way did help, of course. As evident in the above graph, the spreads crossed as soon as the goal went in and got progressively worse. Tough on the world champions, and the Arsenal quip I made earlier turned out to have rung true.